SlideShare a Scribd company logo
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 223
Artificial Intelligence and the Field of Robotics: A Systematic Approach
to Cybersecurity and Healthcare Systems
Usman Ibrahim Musa1, Aminu Ibrahim Musa2, Sakshi Dua3
1School of Computer Applications, Lovely Professional University, Punjab, India.
2Depertment of Information Technology, Ecole De Superieure De Gestion Et De Technologie, Benin.
3School of Computer Applications, Lovely Professional University, Punjab, India.
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - A systematic review of cybersecurity and
healthcare systems from the Artificial Intelligence (AI) and
robotics perspective for the past 6 years is presented in this
research. Cybercriminals nowadays are always researching
new ways to break into corporate networks andstealsensitive
data. People frequently adhere to the same fundamental
security precautions on a daily basis, and as they use more
devices at work, for security experts, maintainingthe dataand
keeping them current isbecoming moreandmorechallenging.
AI in cybersecurity is gaining importance as it contributes to
overcoming the aforementioned difficulties. Additionally, the
advances brought about by AI and the field of robotics have
proved advantageous for the healthcaresector. Withtheuseof
AI techniques like deep learning and machine learning, a
number of healthcare systems have been developed that
autonomously diagnose various diseases frommedicalimages
and further generate reports based on the findings. This
research focuses on the role of AI and the field of robotics in
enhancing the cybersecurity and healthcare sector. The
research's literature demonstrates that AI in healthcare and
cybersecurity is still a new and innovative field that needs to
be studied further in the future. Researchers may utilize this
study to get helpful tips and knowledge for their next work.
Key Words: AI, Robotics, Cybersecurity, Healthcare.
1. INTRODUCTION
Robotics and artificial intelligence are two major fields of
science and engineering research. These terms are often
used interchangeably to describe the development of
technologies that help make machines intelligent. However,
there is a significant difference between the two. AI is what
enables robots to function like humans, while robotics is the
study of how to make them do so. Together, these
technologies hold great promise for the future.
A topic that in recent years has become familiar to just
about everyone. Hardly a day goes by without news media
reporting on the latest cyber-attack, whether it's conducted
by criminal or government organizations. The study of
strategies we may employ to lessen the possibility of such
assaults, wherever they come and for whatever reason, is
known as cyber security. A paper surveys the field of robot
learning from demonstration, which is a key aspect of AI in
robotics. The authors provide an overview of the different
techniques used for robot learning from demonstration,
including inverse reinforcement learning, apprenticeship
learning, and behavioural cloning. They also discuss the
challenges and future directions of this field [1].
A surveys the field of AI-based intrusion detection systems,
which are a key aspect of using AI for cybersecurity. The
authors provide anoverview ofthedifferenttechniquesused
for AI-based intrusion detection, including rule-based
systems, signature-based systems, and anomaly-based
systems. They also discuss the challenges and future
directions of this field [2]. This research’s goal is to provide
an overview of AI from the perspective of cybersecurity,
including what it is, how we may define it, and how we can
use it to try to enhance the security features of both
businesses and our own personal life. We may conceive of it
as attempting to counteract any threat resulting from our
reliance on and usage of information and communication
technology. A paper surveys the field of robotic security
systems, which is the intersection of robotics and
cybersecurity. The authors provide an overview of the
different types of robotic security systems, including those
used for surveillance,reconnaissance,andsearchandrescue.
They also discuss the challenges and future directionsofthis
field [3]. If you think about it for a moment, this not only
includes using the smartphones tablets, and desktop
computers that we use for work, personal, business, or
leisure, but all the aspects of everyday life that depend on
the use of information technology. A research discusses the
challenges and future directions of cybersecurity for
industrial control systems, which are a key aspect of the
intersection of AI, robotics, and cybersecurity. The authors
highlight the unique challengesofsecuringindustrial control
systems and the importance of developing new security
technologies and standards to address these challenges [4]
Because information technology is so prevalent, problems
with cyber security affect all of our systems and gadgetsthat
are connected to the Internet. Almost every part of our
working life, including the functioning of factories, transit,
and offices globally are included in this, as well as cars for
private and public transportation, the infrastructure
bringing power and water to our houses, and many other
areas. Since practically every part of ourlifenowdepends on
information and communications technology,cybersecurity
has evolved into a basic requirement for everyone. At the
same time, we are aware of the numerous ways in which
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 224
modern information processing systems are susceptible to
assault. One more research discusses the techniques and
challenges of AI-based malware detection, which is another
key aspect of using AI for cybersecurity [5]. It is easy to
argue that our increasingly linked world is theissueandthat
we should change how we interact with it. However, in most
cases, going back is impossible, and in truth,wealmostlikely
don't want to. Modern information and communication
technologies have a significant positive impactonourability
to work from home, increase productivity, and engage in a
variety of previously unimagined kinds of communication
and social contact. If we accept that information and
communications is here to stay, what are we going to do
about the major security threats weall face?Inthisstudy,we
will introduce some of the techniques that can be used to
reduce these threats, especially from the AI and Robotics
perspective. It is important to realize that providingsecurity
is not just about more and better technology.
A contemporary healthcare system is made up of several
components, each of which gathers and analyses data.
Massive volumes of data are produced by healthcare
providers, intermediaries,andgovernmentprogrammeslike
Medicare and Medicaid. Patients can provideinformation on
the care they get, their health state, the results of their
treatment, and related expenditures. Nearly all of thesedata
are now digitised, and some of them may be used for
artificial intelligence research. Artificial intelligence has a
wide range of applications in the medical field, including
improving diagnostic accuracy, performing robotic
procedures, discovering potential drug candidates, and
choosing the most effective therapies for particular patients
However, much like any technology or breakthrough,
artificial intelligence creates ethical questions that its
creators, users, and significant stakeholders like patients
may want to take into consideration [26]. We will call
attention to the ethical ramifications of some components of
the healthcare system that, in our opinion, users and
developers of AI systems should consider. Here, we'll
concentrate on a specific subset of artificial intelligence
applications that are most closely associated with the
provision of healthcare services. What are the moral
dilemmas, though? They are many. AI model systematic
mistake is particularly detrimental to the healthcare
industry. Considering that the results of these models may
have an impact on crucial and even life-and-death choices
[27]. Sometimes these deliberate mistakes can result in
discriminatory judgments, especially if they target entire
groups of sociallydisadvantagedindividuals,suchaswomen,
children, persons of colour,orthosewithpoorincomes. With
that being said, we will be discussing some points to take
care of when it comes to robots in healthcare. The lack of
transparency in AI models is one sort of ethical issue that is
particularly pertinent to this technology. It's sometimes
challenging or impossible to determine how AI derives its
judgments [28]. Particularly if the AI makes use of machine
learning techniques, which implies that the models are
always evolving depending on the data they are using.
Because physicians and healthcareinstitutionsdependon AI
developers to produce tools and technologies that are
reliable and efficient to employ on their patients, this is a
particularly serious issue in the context of healthcare [29].
However, there are currently few guidelines or rules for
assessing the efficacy and safety of many AI-based medical
solutions. However, doctors and other healthcare workers
are responsible ethically and legally for the choices thatAIis
increasingly guiding. Physicians and health care facilities
who use AI in ways that may have an impact on healthcare
choices must be aware of the limitations of the techniques,
data, and models when they are applied to their specific
patient populations. In this research, we'll concentrate on
the ethical problem of competing or conflicting interests.
This issue arises particularly in the area of healthcare.
Robots are being employed for a variety of minimally
invasive surgeries. Many modern hospitals feature robots
that function occasionally in lieu of surgeons and othersthat
help doctors. This is where artificial intelligence, specifically
the field of robotics came in and had a big influence on
healthcare. Some of the algorithms that were linked to those
robots aided them in doing activities depending on the
instructions given and trained to them with very good and
high precision.
RESEARCH QUESTIONS
1. What are the general problems in Cybersecurity?
2. What are the general problems in Healthcare?
3. What is the significance of AI and the field of
robotics?
4. What are the various characteristics of AI.
5. What are the challenges of AI in Healthcare and
Cybersecurity and how to overcome it?
6. What is the research gap existing in AI in
Cybersecurity and Healthcare?
7. What is the future of AI from a Cybersecurity and
Healthcare perspective?
We have compiled the research questions listed above,
and the information from studies on Artificial Intelligence
and robotics, Cybersecurity, and Healthcare is used to
further answer the questions.
WHAT ARE THE GENERAL PROBLEMS IN
CYBERSECURITY?
Cybersecurity is a field that deals with protecting
information, communication, and networks from malicious
attacks. Attackers use cyberspace to carry out their crimes;
thus, it's crucial to secure them. Governments and
corporations need to look after their systems and data since
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 225
anyone can access the internet without permission.
However, not all security measures are good when
protecting the internet.
The worldwide web has become a haven for cybercrime in
recent years. Hackers have found many new ways to exploit
systems and data. Many attacks target government systems.
This is because our system of government is involved in
much of our politics. Other targets are corporations that
handle our country's financial wealth.Manycybercrimes are
committed by state agencies or other high-profile
organizations. They're capable of carrying out dangerous
plans in secrecy. Fortunately, there's a lot of work being
done to secure cyberspace. A few of the most important
general problems include:
1. Increase in Cyberattacks: The number of cybercrimes
continues to grow annually as criminal organizations try to
capitalize on their efforts, such as ransomware and crypto-
jacking. However, in 2021, one of the biggest concerns was
the rise of this type of crime. The number of cyberattacks in
2021 increased by 50% over the previous year. However,
certain regions were hit harder by the attacks, such as
education, healthcare, and research. This mightindicatethat
cyber threat actors are concentrating their efforts inregions
where they are most exposed. An attack rate that has risen
so quickly bodes ill for 2022. Cyber threat actors' use of
automation, deep learning, and automation to improvetheir
techniques will only lead to a rise in the number and
intensity of attacks.
2. Ransomware attacks are on the rise: Attacks involving
ransomware are increasing. In 2017, the WannaCry
epidemic brought ransomware to public attention. Ever
since, a sizable number of ransomware businesses have
emerged, posing a costly and visible threat to all businesses.
In 2021, ransomware organisations shown their ability and
willingness to impact businesses in addition to their
immediate targets.. The most famousexampleistheimperial
pipeline hack. One of the primary pipelines used by the
ransomware gang Dark Side was shut down.
3. Mobile devices bring new risks: The implementation of
Bring Your Own Device (BYOD) rules is another result of the
transition to remote working. Organizations can increase
employee productivity and retention by allowing them to
work from their own devices, but this practise also offers
important information about security and susceptibility to
diseases that might endanger company systems and
solutions. You become incapable of responding.
Cybercriminals have modified their ways in 2021 to
capitalise on the use of mohiles that rises. Triada, FlyTrap,
and MasterFred malware, among other mobile malware
trojans, have all recently surfaced. These mobile trojans
approach the target device and request the required rights
through lax app store security measures, social media, and
other similar strategies.
WHAT ARE THE GENERAL PROBLEMS IN HEALTHCARE?
1. Concerns about health equity: The health sector has
long acknowledged that different demographic groups
experience varied levels of health care. These discrepancies
go beyond only salaries and medical expenses. On the other
hand, environmental influences have a significant effect on
health and wellbeing. The zip code is one of these elements,
also referred to as the social determinants of health. racial
and cultural diversity, the quality of the air and water, and
access to jobs, housing, education, transit, and wholesome
food. In certain areas, enduring racial and social inequality
has also put generations' worth of health at risk. All of these
factors have an effect on a person's overall health and
capacity to get healthcare. Health crises for the underserved
sometimes include hospitalisation or emergencyroomvisits
and incur considerable medical expenses.
2. Opportunities (and pitfalls) of technology: The current
health issue has numerous opportunities but also has the
potential to cause a lot of issues if not properly addressed.
Data are being used more and more in health. The difficulty
is in managing this ocean of data. According to a Frontiers in
ICT research, healthcare professionals and health systems
were already producing about 80MB of data perpatientyear
before the epidemic. In addition to information from
electronic health records (EHRs), this data also contains
information and detailssuchaddresses,demographics,claim
and insurance information, payment history, and schedules.
3. Expensive medical bills: The exorbitant expense of
healthcare is arguably the most serious issue facing our
present healthcare system. More than 45% of American
people say it is difficult to afford medical care,andmorethan
40% say they pay for treatment, according to a poll by the
Kaiser Family Foundation. Healthcare costs are changing
people's behaviour, with many avoiding a doctor when ill or
skipping check-ups altogether. A quarter of Americans
cannot afford the prescriptions they need and may skip
doses or skip prescribed medications. Each of these
behaviours can lead to serious health problems and,
therefore, increased medical costs.
WHAT IS THE SIGNIFICANCE OF AI AND THE FIELD OF
ROBOTICS?
Robots are becoming increasingly advanced both
technologically and structurally. The primary focus of
robotics today is on repairing and saving lives. For example,
doctors use robot arms in hospitals to perform complicated
surgeries without putting their patients at risk. AI is quickly
becoming essential in many areasoflifeincludinghealthcare
and cybersecurity. This is due to the fact that it saves lives,
reduces costs and makes life easier. However, there are still
many unknown with AI, which is why it is significant to
consider the positives and negatives before implementing
this technology in both healthcare and cybersecurity. AI has
a lot of potential in healthcare; it can perform complex tasks
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 226
and can help doctors treat patients more effectively. For
example, it can asset physicians in diagnosing and treating
diseases and also assist them in performing triage and
radiology procedures. Reinforcement learning programs
help medical professionals save lives by performing life-
saving surgeries on human beings. In addition, predictive
models help medical professionals managepatients’ records
and identify issues with patient care systems. Additionally,
AI helps with patient counselling by assisting with diagnosis
and providing psychological support to patients and
essentially has the potential to revolutionize our healthcare
system
WHAT ARE THE CHALLENGES OF AI IN CYBERSECURITY
AND HEALTHCARE AND HOW TO OVERCOME IT?
AI is the term given to describe the advancement of
computers to perform tasks that were once reserved for
humans. It has the potential to revolutionizemanyaspects of
our lives- from health and education to military and
commercial sectors. However, it is also a source of
considerable concern as it raises questions regarding ethics,
safety, and accountability.
AI is still in its infancy so there are still many challenges to
overcome. For instance, AI is not very good at handling
controversial or negative data, as it can have a conflictive
effect on the system. It is also susceptible to adversarial
behaviour since hackers can use AI for their own purposes
by programming it against the systems they target. Many
Cybersecurity experts believe that AI will be most beneficial
in situations involving classified data, where security
measures are necessary but impossible. The cybersecurity
industry is getting bigger every year. As more and more
people rely on technology in their daily lives, it's important
to make sure these devices and computers are safe from
hackers. There are somecybersecurityissuesthatareeasyto
fix. For example, many people use the same password for
their social media accounts and email girlfriend accounts.
This makes it easier for hackers to steal passwords and use
them to break into those accounts. They can then steal your
personal information and use it to commit identity theft.
Another problem with cybersecurity is that ordinary people
are not fully aware of how to protect themselves. They are
also unaware of the dangers of opening emails or
attachments that appear to come from people they know.
These emails may contain viruses thatcanharmyourdevice.
It could also be a phishing scam that steals your personal
information.
The fundamental healthcare issue has a few other remedies
as well. Collaboration between local, state, and federal
governments, as well as healthcare professionals, is
necessary to find answers to the problem of excessive
healthcare expenditures. To address environmental
variables and enhance access to healthcare in marginalized
neighbourhoods, it is possible to employ housing,
transportation, and collaborations with churches and non-
profit health groups. To satisfy the demands of patients,
healthcare managers might put up a several kinds of
programs. Example, telemedicine can help patients who do
not have access to transportation, as is the case in many
rural places, yet internet connectivity is still anissue.Elderly
home care is one of the other initiatives. a healthcare team
that prioritises community involvement and patient care.
WHAT IS THE RESEARCH GAP EXISTING IN AI IN
CYBERSECURITY AND HEALTHCARE?
Artificial Intelligence and Cybersecurity are two of the most
important technologies today. CybersecurityandHealthcare
are also two areas that are rapidly developing, expanding,
and gaining more relevance in our daily lives. However, AI
technologies have many flaws that need to be addressed-
which is why more research is needed to make them more
useful. Both areas are in a stage of development; therefore,
they have many challenges to overcome before they can
revolutionize our lives.
AI has a lot of potential in Cybersecurity and Healthcare
since it can help detect and prevent cybercrime when we
take the field of Cybersecurity. And in healthcare, it can help
diagnose a disease from its very early stage and also reduce
the workload on the doctors as well.
Currently, Cybercrime is mostly detected through human
involvement, which is slow and error-prone.AIcanalsohelp
with the investigation process by analysing data collected
from various sources andidentifyingpotential threads.It can
also help with countermeasures by developing mechanisms
that stop attacks before they happen.Withthatbeingsaid, AI
has the potential to become an invaluable tool for
Cybersecurity and Healthcare when applied practically.
WHAT IS THE FUTURE OF AI FROM A CYBERSECURITY
AND HEALTHCARE PERSPECTIVE?
Robotics and artificial intelligence have many exciting
applications that will become clear once they're ready for
use by the public. For now, these technologies are primarily
used in scientific research or in niche applications by
professionals only. However, there's no shortage of interest
from amateurs who want to create their own robot
companions. It's clear that these technologies have a huge
future.
AI has many applications- from natural language
processing to pattern recognition and will change our lives
in many years when we take a look at how it changes and is
changing our daily lives from the perspective of
cybersecurity and healthcare. It is very obvious that the AI
has a very large and good future.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 227
2. METHODOLOGY
Several research papers usedin this researchwereexplained
in this part. Consequently, weprovide and clarify the current
surveys in all the areas of this research including AI and
Robotics, Cybersecurity, and Healthcare.
A. APPLICATIONS OF AI IN CYBERSECURITY DEFENCE
The AI model provides highlypowerfuldefensivecapabilities
for cybersecurity protection that will help defend various
systems against cyberattacks and support digital forensic
investigations. Having said that,wehighlightafewoftheuses
of AI in cybersecuritydefense.Additionally,weencouragethe
reader of this research to look at these publications for
additional information on AI's role in cybersecurity
protection.
i. AI for malware detection and classification: This
term simply stands for “MaliciousSoftware”whichisactually
dangerous in short. It is a document that contains programs
or codes which is mostly delivered over a network [1] [2]. It
is produced or planned to employ various methods, such as
ransomware, spyware, viruses, trojans, and adware, to
damage targetcomputer systems, mobile devices,andonline
applications. [3] [4].
Several algorithms and techniques have been used to
detect malware [5].DetectionofmalwareusingAItechniques
can be done when a model is trained using a dataset that can
help in classifying the type of malware [6].
ii. AI for network intrusion detection: Many
programmers created and suggested network intrusion
detection solutions. Ding et al. [7] presented a real-time
anomaly detection technique andwassuccessfulinachieving
high accuracy. Additionally, after conducting K-means
clustering, Alom andTaha[8]attainedarespectableaccuracy
of 91.86%. Chen et al [9] provided an example of how deep
convolutional neural networks (DCNNs) are used to identify
DDoS assaults. Some other researchers who worked on the
same topic include Mirsky et al. [10], Biswas [11], Clements,
et al. [12], and Xia et al. [13].
iii. AI for traffic identification and classification: At a
time, several applicationsare flowinginanynetwork,andthe
one and single most important phase in identifying and
recognizing multiple classes is the use of network traffic
classification. A researcher [14] utilized a deep learning
model to distinguish the flowing of traffic in a network after
diving it into 25 protocols, he was successfully able to get
100% and 91.74%, depending on the type of protocol.
Another research [15] used a Convolutional Neural Network
(CNN) model to distinguish the classes of traffic and also try
to recognize the application category.
iv. AI for spam detection: Spam emails, to put it simply,
are any unwelcomeor virus-containing emails. Inadditionto
acting as a detector of all those viruses, spam detection
systems also work as a preventer of emails by stopping them
from introducing viruses into one's inbox. One of the
techniques that developers have suggested is an auto-
encoder that functions and further distinguishes spam mail
by Mi et al. [16], with a 95% accuracy rate. A different
researcher created a machine-learning approach and
algorithmic phishing email detectionsystem[17].Thereader
of this paper can refer to the following works related to this
by Aksu et al. [18], Yi et al. [19], and Benavides et al. [20].
v. AI for insider threat detection: A document that
demonstratesandclearlyexplainshowtoexamineandassess
a user's system logs using a DNN or RNN model, as well as
how to find abnormalities that might lead toaninsiderthreat
incident. Tuor et al. [21] described how to do this.
vi. AI for digital forensics: AI technology become most
significant in investigations nowadays and also improvesthe
methodsand ways of detecting cybercrime.Thespecialistsof
forensics found this very useful as it helps them in effectively
and quickly find the actual source and cause of the problem,
on the other hand, the use of AI in digital forensic saves a lot
of money and time. Some machine-learning techniques or
algorithms have been utilized to classify file fragments. For
example, papers are written by Beebe et al. [22], Axelsson et
al. [23], and Calhoun & Coles [24]. Another researcher [25]
proposed a technique that works based on deep learning for
file fragment classification.
B. APPLICATIONS OF AI IN HEALTHCARE
i. Disease Detection systems: One of the most
significant tasks in healthcare is the detection of various
diseases. it lessens the stress on doctors, because those
systems may be replaced to run automatically instead of
manually for various other duties. . Researchers have
suggested a method in 2019 that might assist physicians in
identifying and categorizing skin conditions, such as
melanoma and eczema [26]. A machinelearning algorithm is
used in detecting skin cancer where it differentiates healthy
skin from diseased one and high accuracy was achieved [27].
Many systems for brain cancer classification have also been
invented by developers which include an approach by Sha et
al [28], they developed a system using deep Convolutional
Neural Networks (DCNNs) to detect brain tumors after
Magnetic Resonance Imaging (MRI) generated the high-
quality images of the inside of the brain. The reader of this
research can also go through these articles for disease
detection systems: Ahmad et al. [29], Ahmad et al. [30],
Shabbir et al [31], and Hussain et al [32].
ii. Test Analysis and Diagnosis: Because all those AI-
based apps will haveahugeinfluenceoninterpretingmedical
scans, including X-rays, MRI pictures, CT scans, and many
more, it is getting simpler for physicians to simply
comprehend the problemoftheirpatientswhenthereisanAI
application. As the effort associated with scanninganalysisis
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 228
lessened, medical physicians feel more at ease [33]. The AI-
based approach will assist in realizing and comprehending
whether any gene might cause cancer while evaluating
biological data such as DNA and RNA [34]. The AI can help
identify any disease risk or existence. The characteristics
depend on outside factors [35]. It further helps in alerting
people about any disease-infected area [36].
iii. Chatbots:Thesedays,hospitalsandotherclinicshold
a number of websites, mobile applications, and web
applications. These websites, mobile applications, and web
applications feature chatbots that act to aid patients directly
from where they are and try to learn more about their health
issues [37]. Every time a patient enters a hospital, the first
thing the medical staff does is screen the patient to learn
about their beginning circumstances. In this situation, AI
chatbots can take the role of these time-consuming
procedures [38]. Additionally, chatbots may be used as
interacting agents between language processing and speech
recognition technology [39]. As a whole, majority of the
modern healthcare institutions have these kinds of chatbots
which help patients in different ways [40].
iv. Health Monitoring: When it comes to patient
prevention through condition monitoring, this iscrucial.The
monitoring system may occasionallybeabletokeepapatient
in their present state when an illness is caught early by
informing the doctors. Algorithms and AI approaches are
used to assist it. A smart health monitoring system has been
proposed by some researchers [41], the system is capable of
keeping track of patients’ healthand it alsocontainsafeature
that enables patients' families to access and check on their
patient’s health status. Anandh [42] created a system that
uses AI algorithms to provide body temperature. Papers
written by Soppimath et al [43] and Srinivasan et al. [44] can
be checked to get more health monitoring systems that were
trained based on AI algorithms and techniques.
v. Digital Consultation: The world is getting
increasingly digital; thus, this is a fairly broad area. A digital
consultation is just a video call between a doctor or other
healthcare professionaland a patient made possible through
a smartphone or online application. Through these tools, the
patient and the doctor will communicate. Examples of the
evolution of digital healthcare include patients' engagement
in the development and higher expectations for patient
access to healthcare [45, 46, 47]. Most primary care doctors
can now operate from home, and in this scenario, digital
consultation will undoubtedly occur [48, 49].Thisreadercan
check [50] and [51] to get more ideas about digital
consultations and their cost-effectiveness.
C. DATA SOURCE
The literature in this paper is made up of several research
publications and articles from different sources. Fig.1 shows
the pictorial or graphical representation of the data sources
used in this research and their respective percentages.
Additionally, wehave madea table of the databasesandtheir
respective URLs that were allused in this researchwhichcan
be seen in fig. 2.
D. EXPLORATION CRITERIA
As mentioned in the abstract that this research will focus
more on the area of AI and robotics in cybersecurity and
healthcare forthe past 6 years which is from2017to2022.In
light of the foregoing, we gathered all the references and
brought out the percentage of the papers used in this
research for each and every respective year. The pictorial
representation of the same is shown in fig. 3 where all the
percentages are clearly stated.
Fig.1. Research Papers from Data Sources.
Fig.2. Database Engines and their URLs
Fig.3. Percentage of Research Papers from 2017 to
2022.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 229
Cybersecurity is an ever-evolving field, and the systems
developed in the past five years have been instrumental in
helping protect individuals and organizations from cyber
threats. In this article, we will take a look at some of the most
important cybersecurity systems developed in the past five
years in Table.1. Let's first examine how machine learning
(ML) and artificial intelligence (AI) have evolved in the field
of cybersecurity. Systems that can identify and respond to
cyber threats in real-time have been developed using AI and
ML. These technologiesarecapableofanalyzingvastvolumes
of data to spot trends and abnormalities that can point to an
impending attack. Systems that can recognize and react to
harmful codehave also been created using AI and ML,aswell
as systems that can detect and respond to phishing attacks.
These are just a few of the many cybersecurity systems
developed in the past six years. As the field of cybersecurity
continues to evolve, new and improved systems will be
developed to protect individuals and organizations from
cyber threats.
Table 1. Summary of AI-Based Cybersecurity Systems Developed in the past 6 years.
[8] 2017 Cybersecurity network intrusion
detection with unsupervised deep
learning
Attained a respectable accuracy
of 91.86%
Usability issues
[53] 2017 Convolutional neural networks'
ability to identify new assaults is
evaluated.
The CNN model obtained an
81.57% of accuracy rate.
High dimensional data
[54] 2017 developed a recurrent neural
network-based intrusion detection
system (RNNs)
The RNN model has an 83.28%
detection rate in the binary
classification, according to the
results.
Personal Integrity
[55] 2017 An innovative fuzziness-based
semi-supervised learning strategy
that uses unlabelled data with
supervised learning algorithm
assistance improves the classifier's
performance for IDS.
Obtained very high accuracy on
the proposed algorithm.
The accuracy of the J48,
Naïve Bayes, NB tree,
Random forests, Random
tree, multi-layer
perceptron, and Support
Vector Machine (SVM) is
lower than the proposed
algorithm
[57] 2018 A safe malware detection system
using encryption
Achieved 98.93% Efficiency
[58] 2018 An Android malware family
categorization has been proposed,
along with a representative sample
selection.
FalDroid – 94.2% Usability
[59] 2018 for unsupervised feature learning, a
non-symmetric deep autoencoder
(NDAE) has been suggested.
a training time reduction of up
to 98:81% and an
improvement in accuracy of
5%.
Huge amount of complex
[60] 2019 a method to identify malware based
on the incidence of opcodes
The suggested method can
identify the virus with about
100% accuracy.
Less number of datasets.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 230
[61] 2019 Using data and APIs, to identify
malware
AUC 99.3% Privacy
[56] 2019 examination of extracted
characteristics from big-data
sources in real time.
True Positive Ratio, Precision,
Recall and F1 > 99%, FPR <
0.1%
Effectiveness
[63] 2019 It was showed how to find malware
payloads in a number of file types,
including Portal Document File.pdf
and Microsoft Document File.doc.
The accuracy of finding
ransomware was 91.7% and
94.1%, respectively.
Limited incremental rate
[64] 2019 Deep learning-based proposed
method for virus detection using
behaviour graphs
Accuracy of 98.60% Unstructured
[66] 2020 proposed a dynamic technique for
detecting and predicting Windows
malware
Prediction – 0.997
FPR of 0.000
FNR of 0.007
Trust
[67] 2020 suggested using a method called
SourceFinder to locate malware
source code repositories.
According to the research, the
suggested method locates
malware repositories with 89%
precision and 86% recall.
Poor understanding of
safety
[68] 2021 They provide a novel approach for
automatic hyperparameter
optimization based on Bayesian
optimization to produce the best
possible DNN design.
BO-GP obtained the highest
accuracy scores, with 82.95%
for the KDDTest+ dataset and
54.99% for the KDDTest-21
dataset. accuracy.
Appropriateness
[86] 2022 ML-based malware classification for
Android devices using repacked app
detection and removal
Detection Accuracy of 98.2% Efficiency
[87] 2022 Malware Threads Classification 98% Accuracy in detecting and
classifying the malware threads
Trust
[62] 2022 Approaches in malware detection
systems that rely on visualisation
The Approach Achieved 100%
Accuracy
Poor and little amount of
dataset to get high
accuracy
Table 2. Summary of Datasets, Samples, and Methodology used in the Past AI-Based Cybersecurity Systems
Reference Title of Paper Methodology Datasets and Samples Used
[86] AndroMalPack: enhancing the
ML-based malware classification by
detection and removal of repacked
apps for Android systems
Nature Inspired Algorithm AndroZoo Dataset
[55] Fuzziness-based semi-supervised
learning approach for intrusion
detection system
Random forests, NB tree, J48,
Naive Bayes, random tree, multi-
layer perceptrons, and SVM
(SVM)
unlabelled samples assisted
with a supervised learning
algorithm.
[54] Deep Learning Approach for
Intrusion Detection Using Recurrent
Neural Networks
Binary classification (Normal,
Anomaly) and five category
classifications using the RNN-IDS
model (Normal, DoS, R2L, U2R,
and Probe).
NSL-KDD dataset
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 231
[87] Binary and Multi-Class Malware
Threads Classification
Naïve Bayes (NB) and Gaussian
Discriminant Analysis (GDA)
MaleVis Dataset
[60] Detection of Advanced Malware by
Machine Learning Techniques
Machine Learning Techniques Kaggle Microsoft malware
classification challenge dataset
[68] Bayesian hyperparameter
optimization for deep neural
network-based network intrusion
detection
Deep Neural Network Algorithms NSL-KDD dataset
[57] A secure encryption-based malware
detection system
Privacy-Preserving Naïve Bayes
Classifier (PP – NBC)
4-Gram API Fragment Sequence
[67] Source finder: Finding malware
source code from publicly available
repositories
Machine Learning Techniques in
detecting the Malware
Not Identified
[62] Disarming Visualization-based
Approaches in Malware Detection
Systems
Visualization-based techniques Mallmg Dataset
[8] Network intrusion detection for
cybersecurity using unsupervised
deep learning approaches
K-means Clustering NSL-KDD dataset
[61] ASSCA: API sequence and statistics
features combined architecture for
malware detection
Dynamic behaviour Malicious samples from virus
Share and VirusTotal, as well as
samples from Windows 7 and
Windows XP system exe files
[63] A novel malware detection system
based on machine learning and
binary visualization
Neural network and deep
learning are used in the detection
of the malware.
Not mentioned
[53] Intrusion Detection Using
Convolutional Neural Networks for
Representation Learning
In testing the set, 17 extra attack
kinds were added, and a new
attack was also found.
NSL-KDD dataset
[66] A dynamic Windows malware
detection and prediction method
based contextual understanding of
API call sequence
Using Markov chain sequence to
depict the link between API
functions to represent malware
and goodware
Intelligent and Security
Informatics Data sets Brazilian-
malware-dataset
[64] Malware detection based on deep
learning of behaviour graphs
Stacked AutoEncoders and the
Behaviour-based Deep Learning
Framework (BDLF)
Malware samples from VX
heaven
[58] Android malware familial
classification and representative
sample selection via frequent
subgraph analysis
FallDroid Genome Project Dataset, Drebin
Dataset, FallDroid – I, FallDroid
- II
[56] An investigative study on motifs
extracted features on real time big-
data signals
Visualization and deep learning
techniques were used
The Virus Share community has
9 virus families, each with 1000
variants
[59] A deep learning approach to
network intrusion detection.
By stacking the NDAEs, a layer-
wise unsupervised
representation learning method
was produced.
KDD Cup’99 and NSL-KDD
datasets
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 232
In the pastsixyears,healthcaresystemshaveundergonea
dramatic transformation. Advances in technology, data
analytics, and artificial intelligence have enabled the
development of new and improved healthcare systems that
are revolutionizing the way healthcare is delivered. These
systems are designed to improve patient outcomes, reduce
costs, and provide better access to care.
Table.3 below presents some of the most significant
healthcare systems developed in the past six years. These
systems are designed to address a variety of healthcare
needs, from patient monitoring and diagnosis to population
health management. Each system is designed to provide a
unique set of features and benefits to healthcare providers
and patients alike. These healthcare systemsare just a few of
the many that have been developed in the past five years. As
technology continues to advance, healthcare providers will
continue to develop new and improved systems to improve
patient outcomes and reduce costs.
Table 3. Summary of AI-Based Healthcare Systems Developed in the past 6 years.
Reference Year Topic Addressed Performance Limitation
[69] 2017 Human Skin Cancer Detection
System
84% Predictive Value and
75% Sensitivity
Unstructured Data
[70] 2017 Skin Cancer Classification Using
Deep Learning
High performance achieved Lack of Elaboration
[71] 2017 Review of Common AI Disease
including, cancer, cardiology,
and neurology
Perfect Analysis in the Review Data Exchange and Safety
[72] 2018 Detection of Onychomycosis
and normal nails
Sensitivity of 96.7% and a
Specificity of 96.7%
Too Much Load of Different
Dataset
[73] 2018 Skin Disease Identification 88% in detection Efficiency
[74] 2018 Diagnosis of Skin Cancer Detection accuracy of 90% Less Flexible
[75] 2019 Approach on Melanoma and
other skin cancer types
99% of Accuracy in
Classifying Skin Cancer
Less amount of Data
[65] 2019 Device Application for Skin
Cancer Detection
They Achieved an overall
accuracy of 75.2% in
detecting the Skin Cancer
using the Application
Detection of Only two
Disease
[76] 2019 Review of AI in Applications in
India
Detailed Review of the Topic Ethical Consideration
[77] 2020 Brain Tumour/Cancer
Detection
CNN Architecture = 86%
VGGNet = 97%
Very less number of the
images used
[78] 2020 Skin Cancer Detection 97.9% Accuracy was achieved Interoperability
[79] 2020 Classification of Skin Cancer Achieved an Accuracy of
94.5%
Poor Documentation
[80] 2021 Detection of Brain Cancer SVMs = 92.4%
Five-layer Custom CNN =
97.2%
Less Amount of dataset
[81] 2021 Review of Common Healthcare
Applications and Projects
Clearly explained about the
algorithms and techniques
Poor Abstraction
[82] 2021 The model can identify
photographs that don't fit into
the eight classifications that are
94.9 Accuracy Safety
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 233
often utilised (Classified as
unknown images)
[83] 2022 Detection and Classification of
Brain Tumour that are
generated by MRI
Overall accuracy of 98.87% in
classification and detection
Huge amount of complex
[84] 2022 Chatbot System for Women’s
Healthcare
96% for prediction of PCOS Restrictions (Only for
Women)
[85] 2022 Detection of Skin Cancer using
different algorithms
Accuracy of the proposed
ensemble is 93.5%
Trust
[52] 2022 Classification of Skin Lesion Overall, of 98% Accuracy in
Classifying Skin Lesion
Privacy
Table 4. Summary of Datasets, Samples, and Methodology used in the Past AI-Based Healthcare Systems
Reference Title of Paper Methodology Dataset and Samples
Used
[84] Intelligent Medical Chatbot System for
Women’s Healthcare
Logistic Regression
Algorithm, Machine
Learning Algorithm, and
KNN.
DialogFlow
[83] A Robust Approach for Brain Tumour
Detection in Magnetic Resonance Images
using Finetuned EfficientNet
Deep Convolutional Neural
Network
Brats2015 Brain Tumour
Dataset
[76] Artificial intelligence in healthcare in
developing nations: The beginning of a
transformative journey
SWOT Analysis Review*
[85] Skin Cancer Detection Using Combined
Decision of Deep Learners
SVM, Naïve Bays, and K-
Nearest Neighbour
ISIC Public Dataset
[71] Artificial intelligence in healthcare: Past,
present and future
Support Vector/ Neural
Networks
Review*
[78] Region-of-Interest Based Transfer Learning
Assisted Framework for Skin Cancer
Detection
Convolutional Neural
Networks (CNNs)
DermIS
[70] Dermatologist-level classification of skin
cancer with deep neural networks
Deep Learning Algorithms Not Specified
[80] Brain Tumour Detection using
Convolutional Neural Network
SVMs, K-NN, multi-layer
perceptron, Naive Bayes,
and random forest
algorithms
HAM10000
[69] A machine learning algorithm for
identifying atopic der-mastitis in adults
from electronic health records
Machine Learning
Algorithms
ISIC Dataset
[52] Skin Lesion Classification System using a K-
Nearest Neighbour Algorithm
K-Nearest Neighbour
Approach (KNN) and
Convolutional Neural
Network
ISIC Public Dataset
[81] Unbox the black-box for the medical
explainable AI via multi-modal and multi
Rule-based Decision
Support System
Review*
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 234
centre data fusion: A minireview, two
showcases and beyond
[72] Deep neural networks show an equivalent
and often superior performance to
dermatologists in onychomycosis diagnosis:
automatic construction of onychomycosis
datasets by region-based convolutional
deep neural network
Convolutional Neural
Networks (CNNs)
Not Identified
[77] Brain Tumour Detection using Deep
Learning Models
Convolutional Neural
Network and VGGNet
HM1000
[82] Skin lesions classification into
eight classes for ISIC 2019 using deep
convolutional neural network and
transfer learning
Deep Convolutional Neural
Network in Addition to
GoogleNet
ISIC Dataset
[79] Analysis of basic neural network types for
automated skin cancer
classification using Firefly optimization
method
Neural and Fuzzy Approach ISIC Dataset
[75] Integrated design of deep features fusion
for localization and classification
of skin cancer
Otsu Algorithm, Alex and
VGG-16 Model
HAM10000
[65] An on-device inference app for skin cancer
detection
Convolutional Neural
Network using Tensorflow
ISIC Dataset
[73] Automated skin
disease identification using deep learning
algorithm
InceoptionV2, InceptionV3,
MobileNet
ISIC Dataset
[74] Diagnosis of
skin diseases using convolutional neural
networks
Convolutional Neural
Networks
ISIC Dataset
The past six years have seen a dramatic shift in the way
healthcare systems are developed and implemented. With
the advent of new technologies and the increasing emphasis
on patient-centered care, healthcare systems have become
more efficient and effective. Table 4 highlighted some of the
various healthcare systems developed in the past six years,
their methodologies, and the datasets & samples used.
ARTIFICIAL INTELLIGENCE AND ROBOTICS
customer service, automate manufacturing processes, and
develop autonomous vehicles. AI is also used to develop
virtual assistants, such as Amazon Alexa and Google
Assistant, which can understand natural language and
respond to voice commands. AI is an ever-evolving field of
research, and its potential applications are limitless.
AI is significantly influencing cybersecurity and healthcare.
AI is being utilized in cybersecurity to detect threats to the
network more rapidly and accurately than ever before. AI-
based systems are able to recognize harmful behavior,
identify malicious actors,andrespondtothreatsinreal-time.
This is helping to reduce the amount of time recognized
takes to detect and respond to cyber threats, as well as
reducing the cost of responding to them.
In healthcare, AI is being used to diagnose and treatdiseases
more accurately and quickly than ever before. Doctors may
make more accurate diagnoses and administer better care
when using AI-based systems, which can analyse vast
The goal of the computer science field of artificial
intelligence (AI) is to develop intelligent machines that can
think and behave like humans. AI is used to develop
computer systems that can solve complex problems,
recognize patterns, and learn from experience. AI systems
can be used to automate tasks, such as scheduling, data
analysis, and decision-making. AI is also used to develop
robots that can interact with humans and the environment.
AI has applications in many industries, includinghealthcare,
finance, and transportation. AI can be used to improve
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 235
volumes of data to find patterns and trends inpatienthealth.
In order to cut costs and increase efficiency, AI is now being
utilized to automate administrative choreslikeappointment
scheduling and insurance claim processing.
AI appears to have a bright future in both cybersecurity and
healthcare. More rapidly and precisely than ever, AI may be
used to detect and address cyber threats. AI may also assist
healthcare businesses better secure patient data by
identifying possible security flaws. Healthcare practitioners
might concentrate on more crucial activities by using AI to
automate menial chores. AImayalsobeusedtoexamine vast
volumes of data and find patterns and trends that can be
utilized to enhance patient outcomes and treatment.Finally,
AI can automate illness diagnosis and treatment, freeing up
medical experts to work on more challenging situations.
Characteristics of Artificial Intelligence
1. Automation: Artificial intelligence (AI) is able to do
things like recognize patterns, make judgements, and solve
problems that would typicallyneedhumanintelligence.Data
analysis, natural language processing, and picture
identification are a few examples of complicated jobs and
processes that AI can automate.
2. Machine Learning: AI is capable of learning from its
environment and experiences. Through machine learning
algorithms, AI can learn from data and use it to improve its
performance. On the other hand, Machine learning is a type
of artificial intelligence (AI) that enables computers to learn
without explicit programming. The goal of machine learning
is to build computer programs that canaccessdata anduseit
to learn for themselves. The learning procedure occurs with
observations or data, such as examples, directexperience,or
teaching, in order to uncover patterns within the
information and enhance future judgements based on the
examples we provide. The basic objective is to enable
computers to learn independentlyofhumansandadapttheir
behavior as a result.
i. Supervised Learning: This kind of machinelearning
algorithm makes predictions using labeled data. A labelled
dataset with input data and the associated predicted output
is used to train the algorithm. After that, the system makes
predictions on fresh, unlabeled data using the labelled data.
ii. Unsupervised Learning: This is a kind of machine
learning method that generates predictions from unlabeled
data. An unlabeled dataset, which consists of input data
without any corresponding predicted output,isusedtotrain
the algorithm. Afterward, the program makespredictions on
fresh, unlabeled data using the unlabeled data.
iii. Reinforcement Learning: This is an algorithm that
uses rewards and punishments to learn. The algorithm is
trained on an environment, which contains input data and
the corresponding rewards or punishments. The algorithm
then uses rewards and punishments to make decisions and
take actions in the environment.
Fig. 4. Types of Machine Learning
3. Natural Language Processing: AI can understand
and process natural language, such as spoken words and
written text. This allows AI to interact with humans in a
more natural way. Similarly, the goal of the artificial
intelligence (AI) branch of natural language processing
(NLP) is to give computers the ability to comprehend,
analyze, and modify human language. To analyze text, NLP
algorithms are employed,allowingcomputerstounderstand
the structure and meaning of thelanguageinorderto extract
insights from text data. NLP can be used to automate tasks
such as sentiment analysis, text classification, and entity
extraction.
4. Adaptability: AI is capable of adapting to changing
environments and conditions. AI can learn from its mistakes
and use the data to improve its performance. On the other
hand, Adaptability in AI refers to the ability of an AI system
to adjust its behavior in response to changes in the
environment or the user’s preferences. This allows the AI
system to remain effective and efficient over time, even as
the environment or user preferences change. This is
important for AI systems that are used in dynamic
environments, such as self-driving cars, where the
environment is constantlychanging.Adaptabilityalsoallows
AI systems to learn from their mistakes and improve their
performance over time.
5. Automated Reasoning: AI can reason and draw
conclusions from data. This allows AI to make decisions and
solve problems without human intervention. On the other
hand, automated reasoning is a subfield of artificial
intelligence (AI) that focuses on using computers to reason
logically about a given problem. Automated reasoning
systems use algorithms to analyze a set of facts and rules to
draw logical conclusions. Automated reasoning systemscan
be used to solve problems in many different areas, such as
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 236
mathematics, law, medicine, engineering, and philosophy.
Automated reasoning can also be used to create new
knowledge by combining existingfactsandrules.Automated
reasoning systems are becoming increasingly important in
the development of AI systems, as they can help to reduce
the amount of manual labor required in problem-solving.
6. Autonomous Agents: AI can act independently and
autonomously. This allows AI to take action without human
input or direction. On the other way, Autonomous agents in
AI are computer programs that can act independently in a
given environment. They are able to perceive their
environment, make decisions, and take actions to achieve
their goals. Autonomous agents are used inmanyareasofAI,
consisting computer vision, NLP, and machine learning.
Autonomous agents can be used to automate tasks, such as
scheduling, planning, and decision-making. They canalsobe
used to interact with humans, such as in virtual assistants,
catbots, and autonomous vehicles. Autonomous agents can
be used to improve the efficiency of existing systems,aswell
as to create entirely new systems.
The Robotics Field
Robotics is a field of engineering that focuses on the design,
construction, and operation of robots. It involves the
application of mechanical, electrical, and computer
engineering principles to the design, manufacture, and
operation of robots. Robotics is used in a variety of
applications, including manufacturing,medical,military,and
space exploration. Robotics engineering involvesthedesign,
construction, and operation of robots. This includes the
development of robotic systems, sensors, and actuators, as
well as the integration of these components into a
functioning robotic system. Robotics engineers must also
consider the safety and reliability of the robot, as well as its
ability to interact with its environment. Robotics engineers
must also consider the application oftherobot.Thisincludes
the development of algorithms for robot control, navigation,
and manipulation. Roboticsengineersmustalsoconsiderthe
ethical implications of their work, as robots are increasingly
being used in a variety of applications, including those
involving human interaction. Robotics engineering is a
rapidly growing field, and the demand for qualified
engineers is increasing. Robotics engineers are in high
demand in a variety of industries, including manufacturing,
medical, military, and space exploration. As the technology
continues to advance, the demand for robotics engineers is
expected to continue to grow.
Robotics is becoming increasingly important in many areas
of our lives. Robotics can be used to automate processes,
reduce labor costs, and increase efficiency. Roboticscanalso
be used to improve safety, reduce humanerror,andincrease
accuracy. Robotics can also be used to explore new
environments, such as space, and to perform dangerous
tasks that would otherwise be too risky for humans.
Additionally, robotics can be used to improve healthcare,
such as through surgical robots and robotic prosthetics.
Finally, robotics can be used to improve the quality oflifefor
people with disabilities, by providing them with more
independence and mobility.
The future of robotics is an excitingone.Roboticstechnology
is advancing rapidly, and it is expected to continue to do so
in the coming years. Robotics will continue to be used in a
variety of industries, from manufacturing to healthcare, and
even in the home. As robots become more capable and more
intelligent, they will be able to take on more complex tasks
and interact with humans in more meaningful ways. In the
future, robots may be able to perform tasks that are
currently too difficult or dangerous for humans to do. They
may also be able to provide companionship, help with
household chores, and provide assistance to the elderly and
disabled. As robotics technology continues to evolve, it is
likely that robots will become an integral part of our lives.
Different Types of Robotics
Fig.5 Few Types of Robotics
1. Surgical Robotics: These are robotic systems that
are used to assist in surgical procedures. They are designed
to improve the accuracy and precision of the surgeon,and to
reduce the risk of complications and errors. Surgical robots
typically consist of a robotic arm attached to a console,
which is operated by the surgeon. The robotic arm is
equipped with various tools and instruments, such as a
camera, scalpel, and forceps, which are used to perform the
surgery.
2. Army Robotics: This is the use of robots and robotic
technology in military applications. This includes the use of
unmanned aerial vehicles (UAVs), unmanned ground
vehicles(UGVs),unmanned underwatervehicles(UUVs),and
other robotic systems for reconnaissance, surveillance,
target acquisition, and other military missions.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 237
3. Security robotics is the use of robots to provide
security services such as surveillance, access control, and
perimeter protection. These robots are typically equipped
with sensors, cameras, and other technologies to detect and
respond to potential threats. They can be used to patrol
areas, monitor access points, and detectintrusions.Theycan
also be used to provide real-time information to security
personnel, allowing them to quickly respond to any security
incidents.
4. Service Robotics: Service robots are designed to
interact with humans and provide assistance in a variety of
tasks. Examples of service robots include vacuum cleaners,
medical robots, and personal assistant robots.
5. AutonomousRobots:Autonomousrobotsare robots
that are capable of making decisions and acting
independently without any human input. Examples of
autonomous robots include self-driving cars, unmanned
aerial vehicles (UAVs), and search and rescue robots.
6. Industrial Robotics: Industrial robots are used in
manufacturing and production processes to automate tasks
such as welding, painting, assembly, and packaging. These
robots are designed to be highly accurate and efficient, and
are often used in hazardous environments.
7. Space Robotics: Space robots are designed to
operate in space and are often used for exploration and
research. Examples of space robots include the Mars rovers,
space shuttles, and satellites.
8. Educational Robotics: Educational robots are
designed to teach studentsaboutroboticsandprogramming.
These robots are often used in classroomsandcanbeusedto
teach students about robotics concepts such as sensors,
motors, and programming languages.
When we talk about how Robotics will impact the
cybersecurity and healthcare sector, we will consider the
following;
Robotics is increasingly being used in the field of
cybersecurity to help protect networks, systems, and data
from malicious attacks. Robotics can be used to automate
and streamline many of the tedious, manual tasksassociated
with cybersecurity, such as vulnerability scanning, malware
detection, and patch management. Robotics canalsobeused
to detect and respond to threats in real-time, allowing for
faster and more effective responses to cyberattacks.
Robotics can also be used to help identify and mitigate
potential threats before they become a problem. By using
machine learning and artificial intelligence, robotics can
analyze data and detect patterns that may indicate a
potential threat. This can help organizations identify and
address potential threats before they become a major
problem.
Robotics can also be used to help organizations better
understand their security posture. By using robotics to
analyze data and identify potential vulnerabilities,
Organizations may enhance their security posturebyhaving
a better understanding of it. In this case, it can be of help to
organizations better protect their networks, systems, and
data from malicious attacks.
Finally, robotics can be used to help organizations comply
with security regulations and best practices. Robotics can
help organizations automate the process of ensuring that
their networks, systems, and data are compliant with
security regulations and best practices. This can help
organizations reduce the risk of non-compliance andensure
that their networks, systems, and data are secure.
Similarly, the impact of the robotics in the healthcare sector
may include the following;
Robotics in healthcare is a rapidly growing field that has the
potential to revolutionize the way healthcare is delivered.
Robotics can be used to automate mundane tasks, reduce
errors, and improve patient outcomes. Robotics can help in
the accuracy improvement and speed of diagnosis and
treatment. For example, Robotic systems can evaluate
medical photos and find anomaliesfasterandmoreprecisely
than humans. This can help to reduce the time it takes to
diagnose and treat patients.
Robotics can also help to reduce the risk of medical errors.
Robotic systems can be programmedtofollowprotocols and
procedures more accurately than humans, reducing the risk
of mistakes. Robotics can also help to improve the safety of
medical procedures. Robotic systemscanbeusedtoperform
minimally invasive surgeries, reducing the risk of
complications and improving patient outcomes.
Robotics can also help toimprovetheefficiencyofhealthcare
delivery. Robotic systems can be used to automatemundane
tasks such as dispensing medications, reducing the amount
of time it takes to complete these tasks and freeing up
healthcare professionals to focus on more important tasks.
Finally, robotics can help to improve access to healthcare.
Robotic systems can be used to provide remote
consultations and treatments, allowing patients to access
healthcare from anywhere in the world. This can help to
reduce the cost of healthcare and make it more accessible to
people who may not have access to traditional healthcare
services.
3. CONCLUSION
In conclusion, AI and roboticsarerevolutionizingtheway we
approach cybersecurity and healthcare systems. AI and
robotics are providing us with more efficient and secure
solutions for both industries, allowing us to better protect
our data and improve healthcare outcomes. AI and robotics
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 238
are also providing uswithnewopportunitiesforautomation,
which can help reduce costs and increase efficiency. The
potential for AI and robotics to revolutionize cybersecurity
and healthcare systems is immense, and it is important to
continue to explore and develop these technologies in order
to maximize their potential. The integration of AI and
robotics in cybersecurity and healthcare systems is a
promising development that could revolutionizetheway we
protect our data and provide medical care. AI and robotics
have already been used to detect and respond to cyber
threats, automate medical diagnosis, andassistwithsurgical
procedures. As technology continues to evolve, it is likely
that AI and robotics will become even more prevalent in the
healthcare and cybersecurity industries. This could lead to
improved security, increased efficiency, and better patient
outcomes. Ultimately, the use of AI and robotics in
healthcare and cybersecurity systems could have a positive
impact on society as a whole.
4. FUTURE DIRECTION
AI in cybersecurity and healthcare is expected tocontinue to
grow in the future. AI-based systems can be used to detect
and respond to cyber threats, as well as to detect and
prevent healthcare fraud. AI may be employed to
automatically analyse massive volumes of data to find
patterns and anomalies that may indicate a security breach
or healthcare fraud. AI can also be used to create more
secure and efficient healthcare systems, such as by
automating the process of scheduling appointments and
managing patient records. In addition, AI can increase the
precision and efficiency of medical diagnosis andtherapy, as
well as to provide personalized healthcare services. Finally,
AI can be used to create more secure and efficienthealthcare
systems, such as by automating the process of scheduling
appointments and managing patient records.
5. REFERENCES
[1] M. Ahmad, “Malware in computer systems: Problems
and solutions,” IJID (International Journal on
Informatics for Development), vol. 9, p. 1, 04 2020.
[2] N. Milosevic, “History of malware,” Digital forensics
magazine, vol. 1, no. 16, pp. 58–66, Aug. 2013.
[3] S. Gupta, “Types of malware and its analysis,”
International Journal of Scientific Engineering
Research, vol. 4, 2013. [Online]. Available:
https://www.ijser.org/researchpaper/Types-of-
Malware-andits-Analysis.pdf
[4] Statista. A number of worldwide internet hosts in the
domain name system (dns) from 1993 to 2019.
[Online]. Available:
https://www.statista.com/statistics/264473/number-
ofinternet-hosts-in-the-domain-name-system/
[5] F. Kamoun, F. Iqbal, M. A. Esseghir, T. Baker, “AI and
machine learning: A mixed blessing for cybersecurity”.
[6] H.S. Anderson, A. Kharkar, B. Filar, B. Roth, “Evading
machine learning malware detection,” Black Hat USA
2017, July 22-27, 2017.
https://www.blackhat.com/docs/us-17/thursday/us-
17-Anderson-Bot-VsBot-Evading-Machine-Learning-
Malware-Detection-wp.pdf, accessed November 6,
2018.
[7] N. Ding, H. Ma, H. Gao, Y. Ma, and G.Tan, “Real-time
anomaly detection based on long short-term memory
and Gaussian Mixture Model,” Computers & Electrical
Engineering, vol. 79, pp. 1-11, 2019.
[8] M.Z. Alom, and T.M. Taha, “Networkintrusion detection
for cybersecurity using unsupervised deep learning
approaches,” In Proceedings of the 2017 IEEE
National Aerospace and Electronics Conference
(NAECON), Dayton, OH, USA, pp. 63–69, 2017.
[9] J. Chen, Y. Yang, K. Hu, H. Zheng, and Z. Wang, “DAD-
MCNN: DDoS attack detection via multi-channel CNN,”
In Proceedings of the 11th International Conference
on Machine Learning and Computing: ICMLC '19, pp.
484-488, 2019.
[10] Y. Mirsky, T. Doitshman, Y. Elovici, A. Shabtai, and A.
Kitsune, “An ensemble of autoencoders for online
network intrusion detection,” arXiv preprint
arXiv:1802.09089, pp. 1-15, 2018.
[11] S.K. Biswas, S. K, “Intrusion detection using machine
learning: A comparison study,”International Journalof
Pure and Applied Mathematics, vol. 118, no. 19, pp.
101-114, 2018.
[12] J. Clements, Y. Yangy, A.A. Sharma, H. Huy, and Y. Lao,
“Rallying adversarial techniquesagainstdeeplearning
for network security, arXiv preprint
arXiv:1903.11688v1, pp. 1-8, 2019
[13] S. Xia, M. Qiu, M. Liu, M. Zhong, and H. Zhao, “AI-
enhanced automatic response system for resisting
network threats,” In M. Qiu (Ed.): SmartCom 2019,
LNCS 11910, pp. 221–230, 2019.
[14] Z. Wang, “The Applications of Deep Learning on
Traffic Identification”, BlackHat, 2015,
https://www.blackhat.com/docs/us15/materials/us-
15-Wang-The-Applications-Of-Deep-Learning-
OnTraffic-Identification-wp.pdf , accessed March 23,
2019.
[15] M. Lotfollahi, R. Shirali, M.J. Siavoshani, and M.
Saberian, “Deep packet: A novel approach for
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 239
encrypted traffic classification using deep learning,”
arXiv preprint arXiv:1709.02656, pp. 1-13, 2017.
[16] G. Mi, Y. Gao, and Y. Tan, “Apply stackedauto-encoder to
spam detection,” In Proceedings of the International
Conference in Swarm Intelligence, Beijing, China, pp.
3–15, 2015.
[17] M. Alauthman,M.Almomani, M.Alweshah,W. Omoush,
and K. Alieyan, “Machine learning for phishing
detection and mitigation,” In: Machine Learning for
Computer and Cyber Security, B. Gupta, and Q.Z.
Sheng, (eds), pp. 1-27, Taylor & Francis, 2019.
[18] D. Aksu, Z. Turgut, S. Üstebay, and M.A. Aydin,
“Phishing analysis of websites using classification
techniques,” pp. 251–258. Springer, Singapore, 2019.
[19] P. Yi, Y. Guan, F. Zou, Y. Yao, W. Wang, and T. Zhu, “Web
phishing detection using a deep learning framework,”
Wirel. Commun. Mob. Comput, pp. 1–9, 2018.
[20] E. Benavides, W. Fuertes, S. Sanchez, and M. Sanchez,
M.” Classification of phishing attack solutions by
employing deep learning techniques: A systematic
literature review,” in Á. Rocha and R. P. Pereira (eds.),
Developments and Advances in Defense and Security,
Smart Innovation, Systems and Technologies vol. 152,
pp. 51-64, 2020.
[21] A. Tuor, S. Kaplan, B. Hutchinson, N. Nicholsand, and S.
Robinson, “Deep learning for unsupervised insider
threat detection in structured cybersecurity data
streams,” arXiv preprint arXiv:1710.00811, pp. 1-9,
2017.
[22] N.L. Beebe, L.A. Maddox, L. Liu, and M. Sun, “Sceadan:
Using concatenated n-gram vectors for improved file
and data type classification,” IEEE Transactions on
Information Forensics and Security, vol. 8, no. 9, pp.
1519–1530, 2013.
[23] S. Axelsson, “The normalised compression distance as
a file fragment classifier,” Digital Investigation, vol. 7,
no. 8, pp. S24–S31, 2010.
[24] W.C. Calhoun, and D. Coles, “Predicting the types of file
fragments,” Digital Investigation, vol. 5, pp. S14–S20,
2008.
[25] Q. Chen, Q. Liao, Z. Jiang, J. Fang, S. Yiu, G. Xi, et al, “File
fragment classification using grayscale image
conversion and deep learning,” In Proceedings of the
IEEE Symposium on Security and Privacy Workshops,
pp. 140-147, 2018.
[26] N. Soliman A. ALEnezi. “A Method of Skin Disease
Detection Using Image Processing and Machine
Learning” Procedia ComputerScience163(2019)85–
92.
[27] Kritika Sujay R, Pooja Suresh Y, Omkar Narayan P, Dr.
Swapna B.”Skin disease detection using machine
learning” IJERT Vol. 9. Issue 3. 2021.
[28] H. A. Shah, F. Saeed, S. Yun, J. -H. Park, A. Paul and J. -M.
Kang, "A Robust Approach for Brain Tumor Detection
in Magnetic Resonance Images Using Finetuned
EfficientNet," in IEEE Access, vol. 10, pp. 65426-
65438, 2022, doi: 10.1109/ACCESS.2022.3184113.
[29] A. H. Abdel-Gawad, L. A. Said and A. G. Radwan,
"Optimized EdgeDetectionTechniqueforBrainTumor
Detection in MR Images," in IEEE Access, vol. 8, pp.
136243-136259, 2020, doi:
10.1109/ACCESS.2020.3009898.
[30] A. S. Musallam, A. S. Sherif and M. K. Hussein, "A New
Convolutional Neural Network Architecture for
Automatic Detection of Brain Tumors in Magnetic
Resonance Imaging Images," in IEEE Access, vol. 10,
pp. 2775-2782, 2022, doi:
10.1109/ACCESS.2022.3140289.
[31] M. Rizwan, A. Shabbir, A. R. Javed, M. Shabbir, T. Baker
and D. Al-Jumeily Obe,"Brain TumorandGliomaGrade
Classification Using Gaussian Convolutional Neural
Network," in IEEE Access, vol. 10, pp. 29731-29740,
2022, doi: 10.1109/ACCESS.2022.3153108.
[32] Mahbub Hussain, Jordan J. Bird, and Diego R. Faria “A
Study on CNN Transfer Learning for Image
Classification”Contributions Presentedatthe18thUK
Workshop on Computational Intelligence, September
5-7, 2018, Nottingham, UK. January 2019 DOI:
10.1007/978-3-319-97982-3_1
[33] A. Kumar and S. Joshi “Applications of AI in Healthcare
Sector for Enhancement of Medical Decision Making
and Quality of Services,” in 022 International
Conference on Decision Aid SciencesandApplications
(DASA)|978-1-6654-9501-1/22/$31.00©2022IEEE
| DOI: 10.1109/ DASA54658.2022.9765041.
[34] “Understanding Cancer using Machine Learning | by
Pier Paolo Ippolito | Towards Data Science.”
https://towardsdatascience.com/understanding-
cancerusing-machine-learning-84087258ee18
(accessed Aug. 14, 2021).
[35] A. Maharana and E. O. Nsoesie, “Use of Deep Learning
to Examine the Association of the Built Environment
With PrevalenceofNeighborhoodAdultObesity,”JAMA
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 240
Netw. Open, vol. 1, no. 4, pp. e181535–e181535, Aug.
2018,doi:10.1001/JAMANETWORKOPEN.2018.1535.
[36] P. Kostkova, “A roadmap to integrated digital public
health surveillance,” Proc. 22nd Int. Conf. World Wide
Web - WWW ’13 Companion, pp. 687–694, 2013, doi:
10.1145/2487788.2488024.
[37] M. Bryant, “Hospitals turn to chatbots, AI for care |
Healthcare Dive,” Healtcare Dive, 2018.
https://www.healthcaredive.com/news/chatbots-
aihealthcare/516047/ (accessed Aug. 14, 2021).
[38] A. Kumar and S. Joshi “Applications of AI inHealthcare
Sector for Enhancement of Medical Decision Making
and Quality of Services,” in 022 International
Conference on Decision Aid SciencesandApplications
(DASA)|978-1-6654-9501-1/22/$31.00©2022IEEE
| DOI: 10.1109/ DASA54658.2022.9765041.
[39] A. Jouman Hajjar, “6 Chatbot Applications / Use Cases
in Healthcare in 2021,” AI Multiple, 2021.
https://research.aimultiple.com/chatbot-healthcare/
(accessed Aug. 14, 2021).
[40] K. Kalinin, “Healthcare Chatbots: Role of AI, Benefits,
Future, Use Cases, Development.”
https://topflightapps.com/ideas/chatbots-in-
healthcare/ (accessed Feb. 16, 2022).
[41] A. Mihat, N. Mohd Saad, E. Shair, A. Aslam and R. Abdul
Rahim, "SMART HEALTH MONITORING SYSTEM
UTILIZING INTERNET OF THINGS (IoT) AND
ARDUINO", Asian Journal Of Medical Technology, vol.
2, no. 1, pp. 35-48, 2022. Available:
10.32896/ajmedtech.v2n1.35-48
[42] R. Anandh and G. Indirani, "Real Time Health
Monitoring System Using Arduino with Cloud
Technology", Asian Journal of Computer Science and
Technology, vol. 7, no. 1, pp. 29-32, 2018. Available:
10.51983/ajcst-2018.7.s1.1810.
[43] V. Soppimath, A. Jogul, S. Kolachal and P. Baligar,
"Human Health Monitoring System Using IoT and
Cloud Technology - Review", International Journal of
Advanced Science and Engineering,vol.5, no.2,p.924,
2018. Available: 10.29294/ijase.5.2.2018.924-930.
[44] C. Srinivasan, G. Charan and P. Sai Babu, "An IoT based
SMART patient health monitoringsystem",Indonesian
Journal of Electrical Engineering and Computer
Science, vol. 18, no. 3, p. 1657, 2020. Available:
10.11591/ijeecs.v18.i3.pp1657-1664.
[45] Regeringskansliet. Vision e-hälsa 2025 –
gemensamma utgånspunkter för digitalisering i
socialtjänst och hälso – och sjukvård.
Socialdepartementet och SKL; 2016.
https://www.regeringen.se/499354/contentassets/7
9df147f5 b194554bf401dd88e89b791/vision-e-
halsa-2025-overenskommelse.pdf Accessed 12 June
2020.
[46] Baird B, Charles A, Honeyman M, Maguire D, Das P.
Understanding pressures in general practice. London:
King’s Fund; 2016
[47] Greenhalgh T, Shaw S, Wherton J, Vijayaraghavan S,
Morris J, Bhattacharya S, et al. Real-world
implementation of video outpatient consultations at
macro, meso, and micro levels: mixed-method study. J
Med Internet Res. 2018;20:e150.
[48] Chen J, Lan YC, Chang YW, ChangPY.Exploring doctors’
willingness to provide online counseling services: the
roles of motivations and costs. Int J Environ Res Public
Health. 2019;17:110.
[49] Allen TD, Golden TD, Shockley KM. How effective is
telecommuting? Assessing the status of our scientific
findings. Psychol Sci Public Interest. 2015;16:40–68
[50] SKR. Statistik om hälso – och sjukvård samt regional
utveckling 2018; 2018.
https://skr.se/ekonomijuridikstatistik/statistik/
ekonomiochverksamhetsstatistik.1342.htmlAccessed
12 June 2020.
[51] Ekman B. Cost analysis of a digitalhealthcaremodelin
Sweden. Pharmacoecon Open. 2018;2:347–54.
[52] M. Q. Hatem “Skin Lesion Classification SystemUsinga
K-Nearest Neighbour Algorithm”, Hatem Visual
Computing for Industry, Biomedicine, and Art (2022)
https://doi.org/10.1186/s42492-022-00103-6
[53] Z. Li, et al. Intrusion Detection Using Convolutional
Neural Networks for Representation Learning. In
International Conference on Neural Information
Processing (pp. 858-866). Springer, Cham,November
2017.
[54] C. Yin et al. Deep Learning Approach for Intrusion
Detection Using Recurrent Neural Networks. IEEE
Access, 5, 21954-21961.
[55] R. Ashfaq, et al. Fuzziness based semi-supervised
learning approach for intrusion detection system.
Fuzziness based semi-supervised learning approach
for intrusion detection system. Information Sciences,
378, 484-497, 2017.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 241
[56] S. T. Ahmed and K.K Patil, “An Investigative study on
motifs extracted features opn real-time big-data
signals”, in Proceedings of the 2016 International
Conference on Emerging Technological Trends
(ICETT), Kollam, India, IEEE, 2016, pp. 1-4. Doi:
10.1109/ICETT.2016.7873721
[57] Z. Lin, X. Fei, S. Yi, M. Yan, X. Cong-Cong and H. Jun, “A
secure encryption-based malware detection system.”
KSII TransactiononInternetandInformationSystems
(TIIS), Vol. 12, no. 4, April 2018, pp.1799-1818. Doi:
10.3837/tiis.2018.04.022.
[58] M. Fan, J. Liu, X. Luo, K. Chen, Z. Tian, Q. Zheng, and T.
Liu, “Android malware familial classification and
representativesampleselectionvia frequentanalysis”
IEEE Transaction on Information Forensics and
Security, Vol. 13, No. 8 August 2018, pp. 1890-1905,
doi: 10.1109/TIFS.2018.2806891.
[59] N. Shone et al. A deep learning approach to network
intrusion detection. IEEE Transactions on Emerging
Topics in Computational Intelligence, 2(1), 41-50,
2018.
[60] S. Sharma, R. Challa, and S. Sahay, Detection of
Advanced Malware by Machine Learning Techniques:
Proceedings of SoCTA 2017, 01 2019, pp. 333–342.
[61] L.Xiaofeng, J. Fangshuo, Z. Xiao, Y. Shengwei, S. Jing
and P. Lio “ASSCA: API sequence and statistics
features combined architecture for malware
detection”, Computer Networks, Vol. 157, July 2019,
pp. 99-111, doi: 10.1016/j.comnet.2019.04.007.
[62] L. S. Fasci, M. Fisichelle, G. Lax, and C. Qian “Disarming
Visualization-basedApproachesinMalwareDetection
Systems” in Computers & Security · December 2022
DOI: 10.1016/j.cose.2022.103062
[63] I. Baptista, S. Shiaeles, and N. Kolokotronis, “A novel
malware detection system based on machine learning
and binary visualization,” 05 2019, pp. 1–6.
[64] F. Xiao, Z. Lin, Y. Sun and Y. Ma, “Malware detection
based on deep learning of behaviour graphs”,
Mathematical Problems in Engineering, Vol.2019,
February 2019, pp. 1-10, doi:
10.1155/2019/8195395.
[65] Dai XF, Spasić I, Meyer B, Chapman S,AndresF(2019)
Machine learning on mobile: An on-device inference
app for skin cancer detection. In: Abstracts of the 4th
international conference on fog and mobile edge
computing, IEEE, Rome, 10-13 June 2019.
https://doi.org/10.1109/FMEC.2019.8795362
[66] E. Amer and I. Zelinka, “ A dynamic windowsmalware
detection and prediction method based oncontextual
understanding of API call sequence”, Computers and
Security, Vol. 92, February 2020, pp. 1-5, doi:
10.1016/j.cose.2020.101760.
[67] M. O. F. Rokon, R. Islam, A. Darki, E. Papalexakis,andM.
Faloutsos, “Sourcefinder:Finding malwaresource-code
from publicly available repositories,” in RAID, 2020.
[68] M. Mohammad, S. Hossain, H. Hisham, H. F. Md Jobair,
V. Maria, K. Md Abdullah, A. R. Mohammad, A.
Muhaiminul I., C. Alfredo, and W. Fan, “Bayesian
hyperparameter optimization for deep neural
network-based network intrusion detection,” IEEE
International Conference on Big Data, 2021.
[69] Gustafson E, Pacheco J, Wehbe F, Silverberg J,
ThompsonW. A machine learning algorithm for
identifying atopic der-matitis in adults from electronic
health records. 2017 IEEEInternationalConferenceon
Healthcare Informatics (ICHI).2017;2017:83---90.
[70] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau
HM,et al. Dermatologist-level classification of skin
cancer withdeep neural networks. Nature.
2017;5427639:115---8
[71] F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q.
Dong, H. Shen, and Y. Wang, ‘‘Artificial intelligence in
healthcare: Past, present and future,’’ Stroke Vascular
Neurol., vol. 2, no. 4, pp. 230–243, 2017, doi:
10.1136/svn-2017-000101.
[72] Han SS, Park GH, Lim W, Kim MS, Na JI, Park I, et al.
Deep neuralnetworks show an equivalent and often
superior performanceto dermatologists in
onychomycosis diagnosis: automaticconstruction of
onychomycosis datasets by region-based con-
volutional deep neural network. PLoS One.
2018;13:e0191493
[73] Patnaik SK, Sidhu MS, Gehlot Y, Sharma B, Muthu P
(2018) Automated skin disease identification using
deep learning algorithm. Biomed Pharmacol
J11(3):1429–1436.
https://doi.org/10.13005/bpj/1507
[74] Rathod J, Waghmode V, Sodha A, Bhavathankar P
(2018) Diagnosis of Skin diseasesusingconvolutional
neural networks. In: Abstracts of the2nd International
lconference on electronics, communication and
aerospace technology. Coimbatore: IEEE.
https://doi.org/10.1109/ICECA.2018.8474593
[75] Amin J, Sharif A, Gul N, Anjum MA, Nisar MW, Azam F
et al (2020) Integrated design of deep features fusion
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 242
for localization and classification of skin cancer.
Pattern Recogn Lett 131:63–70.
https://doi.org/10.1016/j.pa trec.2019.11.042
[76] A. Mahajan, T. Vaidya, A. Gupta, S. Rane, and S. Gupta,
‘‘Artificial intelligence in healthcare in developing
nations: The beginning of a transformative journey,’’
Cancer Res., Statist., Treatment, vol. 2, no. 2, p. 182,
2019, doi: 10.4103/crst.crst_50_19
[77] S. Grampurohit, V. Shalavadi, V. R. Dhotargavi, M.
Kudari, and S. Jolad, ``Brain tumor detection using
deep learning models,'' in Proc. IEEE India CouncilInt.
Subsections Conf. (INDISCON), Oct. 2020, pp.129_134.
[78] R. Ashraf, S. Afzal, A. Rehman, S. Gul, J. Baber, M.
Bakhtyar, I. Mehmood, O. Song, and M. Maqsood,
“Region-of-Interest Based TransferLearningAssisted
Framework for Skin Cancer Detection”, IEEE ACCESS,
Digital Object Identifier
10.1109/ACCESS.2020.3014701
[79] Balaji MSP, Saravanan S, Chandrasekar M, Rajkumar
G, Kamalraj S (2021) Analysis of basic neural network
types for automated skin cancer classification using
Firefly optimization method. J Ambient Intell Human
Comput 12(7):7181–7194.
https://doi.org/10.1007/s12652-020-02394-0
[80] G. Kumar, P. Kumar, and D. Kumar, ``Brain tumor
detection using convolutional neural network,'' in
Proc. IEEE Int. Conf. Mobile Netw. Wireless Commun.
(ICMNWC), Dec. 2021, pp. 1_6.
[81] G. Yang, Q. Ye, and J. Xia, ‘‘Unbox the black-box for the
medical explainable AI via multi-modal and multi-
centre data fusion: A minireview, two showcases and
beyond,’’ 2021, arXiv:2102.01998.
[82] Kassem MA, Hosny KM, Fouad MM (2020) Skin
lesions classification into eight classes for ISIC 2019
using deep convolutional neural network andtransfer
learning. IEEE Access 8:114822–114832.
https://doi.org/10.1109/ACCESS.2020.3003890
[83] H. A. Shah, F. Saeed, S. Yun, J. Park, A. Paul, and J. Kang,
“A Robust Approach for Brain Tumor Detection in
Magnetic Resonance Images Using Finetuned
EfficientNet”, IEEE ACCESS Digital Object Identifier
10.1109/ACCESS.2022.3184113
[84] S. Polekar, S. Wakde, M. Pandare, P. Shingane,
“Intelligent Medical Chatbot System For Women’s
Healthcare” ITM Web of Conference 44, 03020 920
(2022)
https://doi.org/10.1051/itmconf/20224403020.
[85] A. Imran, A. Nasir, M. Bilal, G. Sun, A. Alzahrani, and A.
Almuhameed, “Skin Cancer DetectionUsingCombined
Decision of Deep Learners”, IEEE ACCESS, Digital
Object Identifier 10.1109/ACCESS.2022.3220329.
[86] H. Rafiq, N. Aslam, M. Aleem, B. Issac, and R. H.
Randhawa “AndroMalPack: enhancing the ML-based
malware classification by detection and removal of
repacked apps for Androidsystems”,ScientificReports
| (2022) 12:19534|https://doi.org/10.1038/s41598-
022-23766-w
[87] Ahmed, I.T. Jamil, N.; Din, M.M. Hammad, B.T. Binary
and Multi-Class Malware Threads Classification. Appl.
Sci. 2022, 12, 12528.
https://doi.org/10.3390/app122412528

More Related Content

PDF
IRJET- Use of Artificial Intelligence in Cyber Defence
PDF
Regulating Generative AI: A Pathway to Ethical and Responsible Implementation
DOCX
The Internet of Things (IoT) brings tremendous new capabilities .docx
PPT
Varun IOTs PPT
DOCX
Running Head ANNOTATED BIBLIOGRAPHYANNOTATED BIBLIOGRAPHY .docx
DOCX
INTERNET OF THINGS A STUDY ON SECURITY AND PRIVACY THREATSMd .docx
PDF
An Analysis of Benefits and Risks of Artificial Intelligence
PDF
A Survey Report on : Security & Challenges in Internet of Things
IRJET- Use of Artificial Intelligence in Cyber Defence
Regulating Generative AI: A Pathway to Ethical and Responsible Implementation
The Internet of Things (IoT) brings tremendous new capabilities .docx
Varun IOTs PPT
Running Head ANNOTATED BIBLIOGRAPHYANNOTATED BIBLIOGRAPHY .docx
INTERNET OF THINGS A STUDY ON SECURITY AND PRIVACY THREATSMd .docx
An Analysis of Benefits and Risks of Artificial Intelligence
A Survey Report on : Security & Challenges in Internet of Things

Similar to Artificial Intelligence and the Field of Robotics: A Systematic Approach to Cybersecurity and Healthcare Systems (20)

PDF
The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing R...
PDF
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...
PDF
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...
PDF
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...
PDF
The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing R...
PDF
Cyber Attacks and Crimes in Cyber Security: A Comparative Analysis
PDF
Artificial intelligence andCyberSecurity_zhang2021.pdf
PDF
Cyber security: challenges for society- literature review
PDF
Developing surveillance challenges in theinternet of things
DOCX
Running Head TRENDS IN CYBERSECURITY1TRENDS IN CYBERSECURITY.docx
PDF
Ai Models For Blockchainbased Intelligent Networks In Iot Systems Concepts Me...
PDF
IRJET- Women Security System using IoT
PDF
Challenges and Opportunities of Internet of Things in Healthcare
PDF
Secure Modern Healthcare System Based on Internet of Things and Secret Sharin...
PDF
SECURITY ISSUES IN USING IOT ENABLED DEVICES AND THEIR IMPACT
PDF
Security for the IoT - Report Summary
PDF
AI-Driven Threat Intelligence: Transforming Cybersecurity for Proactive Risk ...
PDF
AI-Driven Threat Intelligence: Transforming Cybersecurity for Proactive Risk ...
PDF
Using Machine Learning to Build a Classification Model for IoT Networks to De...
PPTX
The social aspect of Smart Wearable Systems in the era of Internet-of-Things
The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing R...
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...
The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing R...
Cyber Attacks and Crimes in Cyber Security: A Comparative Analysis
Artificial intelligence andCyberSecurity_zhang2021.pdf
Cyber security: challenges for society- literature review
Developing surveillance challenges in theinternet of things
Running Head TRENDS IN CYBERSECURITY1TRENDS IN CYBERSECURITY.docx
Ai Models For Blockchainbased Intelligent Networks In Iot Systems Concepts Me...
IRJET- Women Security System using IoT
Challenges and Opportunities of Internet of Things in Healthcare
Secure Modern Healthcare System Based on Internet of Things and Secret Sharin...
SECURITY ISSUES IN USING IOT ENABLED DEVICES AND THEIR IMPACT
Security for the IoT - Report Summary
AI-Driven Threat Intelligence: Transforming Cybersecurity for Proactive Risk ...
AI-Driven Threat Intelligence: Transforming Cybersecurity for Proactive Risk ...
Using Machine Learning to Build a Classification Model for IoT Networks to De...
The social aspect of Smart Wearable Systems in the era of Internet-of-Things
Ad

More from IRJET Journal (20)

PDF
Enhanced heart disease prediction using SKNDGR ensemble Machine Learning Model
PDF
Utilizing Biomedical Waste for Sustainable Brick Manufacturing: A Novel Appro...
PDF
Kiona – A Smart Society Automation Project
PDF
DESIGN AND DEVELOPMENT OF BATTERY THERMAL MANAGEMENT SYSTEM USING PHASE CHANG...
PDF
Invest in Innovation: Empowering Ideas through Blockchain Based Crowdfunding
PDF
SPACE WATCH YOUR REAL-TIME SPACE INFORMATION HUB
PDF
A Review on Influence of Fluid Viscous Damper on The Behaviour of Multi-store...
PDF
Wireless Arduino Control via Mobile: Eliminating the Need for a Dedicated Wir...
PDF
Explainable AI(XAI) using LIME and Disease Detection in Mango Leaf by Transfe...
PDF
BRAIN TUMOUR DETECTION AND CLASSIFICATION
PDF
The Project Manager as an ambassador of the contract. The case of NEC4 ECC co...
PDF
"Enhanced Heat Transfer Performance in Shell and Tube Heat Exchangers: A CFD ...
PDF
Advancements in CFD Analysis of Shell and Tube Heat Exchangers with Nanofluid...
PDF
Breast Cancer Detection using Computer Vision
PDF
Auto-Charging E-Vehicle with its battery Management.
PDF
Analysis of high energy charge particle in the Heliosphere
PDF
A Novel System for Recommending Agricultural Crops Using Machine Learning App...
PDF
Auto-Charging E-Vehicle with its battery Management.
PDF
Analysis of high energy charge particle in the Heliosphere
PDF
Wireless Arduino Control via Mobile: Eliminating the Need for a Dedicated Wir...
Enhanced heart disease prediction using SKNDGR ensemble Machine Learning Model
Utilizing Biomedical Waste for Sustainable Brick Manufacturing: A Novel Appro...
Kiona – A Smart Society Automation Project
DESIGN AND DEVELOPMENT OF BATTERY THERMAL MANAGEMENT SYSTEM USING PHASE CHANG...
Invest in Innovation: Empowering Ideas through Blockchain Based Crowdfunding
SPACE WATCH YOUR REAL-TIME SPACE INFORMATION HUB
A Review on Influence of Fluid Viscous Damper on The Behaviour of Multi-store...
Wireless Arduino Control via Mobile: Eliminating the Need for a Dedicated Wir...
Explainable AI(XAI) using LIME and Disease Detection in Mango Leaf by Transfe...
BRAIN TUMOUR DETECTION AND CLASSIFICATION
The Project Manager as an ambassador of the contract. The case of NEC4 ECC co...
"Enhanced Heat Transfer Performance in Shell and Tube Heat Exchangers: A CFD ...
Advancements in CFD Analysis of Shell and Tube Heat Exchangers with Nanofluid...
Breast Cancer Detection using Computer Vision
Auto-Charging E-Vehicle with its battery Management.
Analysis of high energy charge particle in the Heliosphere
A Novel System for Recommending Agricultural Crops Using Machine Learning App...
Auto-Charging E-Vehicle with its battery Management.
Analysis of high energy charge particle in the Heliosphere
Wireless Arduino Control via Mobile: Eliminating the Need for a Dedicated Wir...
Ad

Recently uploaded (20)

PPTX
IOT PPTs Week 10 Lecture Material.pptx of NPTEL Smart Cities contd
PPTX
CH1 Production IntroductoryConcepts.pptx
PPTX
Geodesy 1.pptx...............................................
PPTX
bas. eng. economics group 4 presentation 1.pptx
PPTX
MET 305 2019 SCHEME MODULE 2 COMPLETE.pptx
PDF
Model Code of Practice - Construction Work - 21102022 .pdf
PDF
composite construction of structures.pdf
PPTX
Construction Project Organization Group 2.pptx
PDF
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
PPTX
UNIT-1 - COAL BASED THERMAL POWER PLANTS
PDF
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
PDF
PRIZ Academy - 9 Windows Thinking Where to Invest Today to Win Tomorrow.pdf
PPTX
Sustainable Sites - Green Building Construction
PDF
Mitigating Risks through Effective Management for Enhancing Organizational Pe...
PPTX
additive manufacturing of ss316l using mig welding
PDF
Embodied AI: Ushering in the Next Era of Intelligent Systems
PPTX
CYBER-CRIMES AND SECURITY A guide to understanding
PPTX
KTU 2019 -S7-MCN 401 MODULE 2-VINAY.pptx
PDF
Well-logging-methods_new................
DOCX
573137875-Attendance-Management-System-original
IOT PPTs Week 10 Lecture Material.pptx of NPTEL Smart Cities contd
CH1 Production IntroductoryConcepts.pptx
Geodesy 1.pptx...............................................
bas. eng. economics group 4 presentation 1.pptx
MET 305 2019 SCHEME MODULE 2 COMPLETE.pptx
Model Code of Practice - Construction Work - 21102022 .pdf
composite construction of structures.pdf
Construction Project Organization Group 2.pptx
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
UNIT-1 - COAL BASED THERMAL POWER PLANTS
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
PRIZ Academy - 9 Windows Thinking Where to Invest Today to Win Tomorrow.pdf
Sustainable Sites - Green Building Construction
Mitigating Risks through Effective Management for Enhancing Organizational Pe...
additive manufacturing of ss316l using mig welding
Embodied AI: Ushering in the Next Era of Intelligent Systems
CYBER-CRIMES AND SECURITY A guide to understanding
KTU 2019 -S7-MCN 401 MODULE 2-VINAY.pptx
Well-logging-methods_new................
573137875-Attendance-Management-System-original

Artificial Intelligence and the Field of Robotics: A Systematic Approach to Cybersecurity and Healthcare Systems

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 223 Artificial Intelligence and the Field of Robotics: A Systematic Approach to Cybersecurity and Healthcare Systems Usman Ibrahim Musa1, Aminu Ibrahim Musa2, Sakshi Dua3 1School of Computer Applications, Lovely Professional University, Punjab, India. 2Depertment of Information Technology, Ecole De Superieure De Gestion Et De Technologie, Benin. 3School of Computer Applications, Lovely Professional University, Punjab, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - A systematic review of cybersecurity and healthcare systems from the Artificial Intelligence (AI) and robotics perspective for the past 6 years is presented in this research. Cybercriminals nowadays are always researching new ways to break into corporate networks andstealsensitive data. People frequently adhere to the same fundamental security precautions on a daily basis, and as they use more devices at work, for security experts, maintainingthe dataand keeping them current isbecoming moreandmorechallenging. AI in cybersecurity is gaining importance as it contributes to overcoming the aforementioned difficulties. Additionally, the advances brought about by AI and the field of robotics have proved advantageous for the healthcaresector. Withtheuseof AI techniques like deep learning and machine learning, a number of healthcare systems have been developed that autonomously diagnose various diseases frommedicalimages and further generate reports based on the findings. This research focuses on the role of AI and the field of robotics in enhancing the cybersecurity and healthcare sector. The research's literature demonstrates that AI in healthcare and cybersecurity is still a new and innovative field that needs to be studied further in the future. Researchers may utilize this study to get helpful tips and knowledge for their next work. Key Words: AI, Robotics, Cybersecurity, Healthcare. 1. INTRODUCTION Robotics and artificial intelligence are two major fields of science and engineering research. These terms are often used interchangeably to describe the development of technologies that help make machines intelligent. However, there is a significant difference between the two. AI is what enables robots to function like humans, while robotics is the study of how to make them do so. Together, these technologies hold great promise for the future. A topic that in recent years has become familiar to just about everyone. Hardly a day goes by without news media reporting on the latest cyber-attack, whether it's conducted by criminal or government organizations. The study of strategies we may employ to lessen the possibility of such assaults, wherever they come and for whatever reason, is known as cyber security. A paper surveys the field of robot learning from demonstration, which is a key aspect of AI in robotics. The authors provide an overview of the different techniques used for robot learning from demonstration, including inverse reinforcement learning, apprenticeship learning, and behavioural cloning. They also discuss the challenges and future directions of this field [1]. A surveys the field of AI-based intrusion detection systems, which are a key aspect of using AI for cybersecurity. The authors provide anoverview ofthedifferenttechniquesused for AI-based intrusion detection, including rule-based systems, signature-based systems, and anomaly-based systems. They also discuss the challenges and future directions of this field [2]. This research’s goal is to provide an overview of AI from the perspective of cybersecurity, including what it is, how we may define it, and how we can use it to try to enhance the security features of both businesses and our own personal life. We may conceive of it as attempting to counteract any threat resulting from our reliance on and usage of information and communication technology. A paper surveys the field of robotic security systems, which is the intersection of robotics and cybersecurity. The authors provide an overview of the different types of robotic security systems, including those used for surveillance,reconnaissance,andsearchandrescue. They also discuss the challenges and future directionsofthis field [3]. If you think about it for a moment, this not only includes using the smartphones tablets, and desktop computers that we use for work, personal, business, or leisure, but all the aspects of everyday life that depend on the use of information technology. A research discusses the challenges and future directions of cybersecurity for industrial control systems, which are a key aspect of the intersection of AI, robotics, and cybersecurity. The authors highlight the unique challengesofsecuringindustrial control systems and the importance of developing new security technologies and standards to address these challenges [4] Because information technology is so prevalent, problems with cyber security affect all of our systems and gadgetsthat are connected to the Internet. Almost every part of our working life, including the functioning of factories, transit, and offices globally are included in this, as well as cars for private and public transportation, the infrastructure bringing power and water to our houses, and many other areas. Since practically every part of ourlifenowdepends on information and communications technology,cybersecurity has evolved into a basic requirement for everyone. At the same time, we are aware of the numerous ways in which
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 224 modern information processing systems are susceptible to assault. One more research discusses the techniques and challenges of AI-based malware detection, which is another key aspect of using AI for cybersecurity [5]. It is easy to argue that our increasingly linked world is theissueandthat we should change how we interact with it. However, in most cases, going back is impossible, and in truth,wealmostlikely don't want to. Modern information and communication technologies have a significant positive impactonourability to work from home, increase productivity, and engage in a variety of previously unimagined kinds of communication and social contact. If we accept that information and communications is here to stay, what are we going to do about the major security threats weall face?Inthisstudy,we will introduce some of the techniques that can be used to reduce these threats, especially from the AI and Robotics perspective. It is important to realize that providingsecurity is not just about more and better technology. A contemporary healthcare system is made up of several components, each of which gathers and analyses data. Massive volumes of data are produced by healthcare providers, intermediaries,andgovernmentprogrammeslike Medicare and Medicaid. Patients can provideinformation on the care they get, their health state, the results of their treatment, and related expenditures. Nearly all of thesedata are now digitised, and some of them may be used for artificial intelligence research. Artificial intelligence has a wide range of applications in the medical field, including improving diagnostic accuracy, performing robotic procedures, discovering potential drug candidates, and choosing the most effective therapies for particular patients However, much like any technology or breakthrough, artificial intelligence creates ethical questions that its creators, users, and significant stakeholders like patients may want to take into consideration [26]. We will call attention to the ethical ramifications of some components of the healthcare system that, in our opinion, users and developers of AI systems should consider. Here, we'll concentrate on a specific subset of artificial intelligence applications that are most closely associated with the provision of healthcare services. What are the moral dilemmas, though? They are many. AI model systematic mistake is particularly detrimental to the healthcare industry. Considering that the results of these models may have an impact on crucial and even life-and-death choices [27]. Sometimes these deliberate mistakes can result in discriminatory judgments, especially if they target entire groups of sociallydisadvantagedindividuals,suchaswomen, children, persons of colour,orthosewithpoorincomes. With that being said, we will be discussing some points to take care of when it comes to robots in healthcare. The lack of transparency in AI models is one sort of ethical issue that is particularly pertinent to this technology. It's sometimes challenging or impossible to determine how AI derives its judgments [28]. Particularly if the AI makes use of machine learning techniques, which implies that the models are always evolving depending on the data they are using. Because physicians and healthcareinstitutionsdependon AI developers to produce tools and technologies that are reliable and efficient to employ on their patients, this is a particularly serious issue in the context of healthcare [29]. However, there are currently few guidelines or rules for assessing the efficacy and safety of many AI-based medical solutions. However, doctors and other healthcare workers are responsible ethically and legally for the choices thatAIis increasingly guiding. Physicians and health care facilities who use AI in ways that may have an impact on healthcare choices must be aware of the limitations of the techniques, data, and models when they are applied to their specific patient populations. In this research, we'll concentrate on the ethical problem of competing or conflicting interests. This issue arises particularly in the area of healthcare. Robots are being employed for a variety of minimally invasive surgeries. Many modern hospitals feature robots that function occasionally in lieu of surgeons and othersthat help doctors. This is where artificial intelligence, specifically the field of robotics came in and had a big influence on healthcare. Some of the algorithms that were linked to those robots aided them in doing activities depending on the instructions given and trained to them with very good and high precision. RESEARCH QUESTIONS 1. What are the general problems in Cybersecurity? 2. What are the general problems in Healthcare? 3. What is the significance of AI and the field of robotics? 4. What are the various characteristics of AI. 5. What are the challenges of AI in Healthcare and Cybersecurity and how to overcome it? 6. What is the research gap existing in AI in Cybersecurity and Healthcare? 7. What is the future of AI from a Cybersecurity and Healthcare perspective? We have compiled the research questions listed above, and the information from studies on Artificial Intelligence and robotics, Cybersecurity, and Healthcare is used to further answer the questions. WHAT ARE THE GENERAL PROBLEMS IN CYBERSECURITY? Cybersecurity is a field that deals with protecting information, communication, and networks from malicious attacks. Attackers use cyberspace to carry out their crimes; thus, it's crucial to secure them. Governments and corporations need to look after their systems and data since
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 225 anyone can access the internet without permission. However, not all security measures are good when protecting the internet. The worldwide web has become a haven for cybercrime in recent years. Hackers have found many new ways to exploit systems and data. Many attacks target government systems. This is because our system of government is involved in much of our politics. Other targets are corporations that handle our country's financial wealth.Manycybercrimes are committed by state agencies or other high-profile organizations. They're capable of carrying out dangerous plans in secrecy. Fortunately, there's a lot of work being done to secure cyberspace. A few of the most important general problems include: 1. Increase in Cyberattacks: The number of cybercrimes continues to grow annually as criminal organizations try to capitalize on their efforts, such as ransomware and crypto- jacking. However, in 2021, one of the biggest concerns was the rise of this type of crime. The number of cyberattacks in 2021 increased by 50% over the previous year. However, certain regions were hit harder by the attacks, such as education, healthcare, and research. This mightindicatethat cyber threat actors are concentrating their efforts inregions where they are most exposed. An attack rate that has risen so quickly bodes ill for 2022. Cyber threat actors' use of automation, deep learning, and automation to improvetheir techniques will only lead to a rise in the number and intensity of attacks. 2. Ransomware attacks are on the rise: Attacks involving ransomware are increasing. In 2017, the WannaCry epidemic brought ransomware to public attention. Ever since, a sizable number of ransomware businesses have emerged, posing a costly and visible threat to all businesses. In 2021, ransomware organisations shown their ability and willingness to impact businesses in addition to their immediate targets.. The most famousexampleistheimperial pipeline hack. One of the primary pipelines used by the ransomware gang Dark Side was shut down. 3. Mobile devices bring new risks: The implementation of Bring Your Own Device (BYOD) rules is another result of the transition to remote working. Organizations can increase employee productivity and retention by allowing them to work from their own devices, but this practise also offers important information about security and susceptibility to diseases that might endanger company systems and solutions. You become incapable of responding. Cybercriminals have modified their ways in 2021 to capitalise on the use of mohiles that rises. Triada, FlyTrap, and MasterFred malware, among other mobile malware trojans, have all recently surfaced. These mobile trojans approach the target device and request the required rights through lax app store security measures, social media, and other similar strategies. WHAT ARE THE GENERAL PROBLEMS IN HEALTHCARE? 1. Concerns about health equity: The health sector has long acknowledged that different demographic groups experience varied levels of health care. These discrepancies go beyond only salaries and medical expenses. On the other hand, environmental influences have a significant effect on health and wellbeing. The zip code is one of these elements, also referred to as the social determinants of health. racial and cultural diversity, the quality of the air and water, and access to jobs, housing, education, transit, and wholesome food. In certain areas, enduring racial and social inequality has also put generations' worth of health at risk. All of these factors have an effect on a person's overall health and capacity to get healthcare. Health crises for the underserved sometimes include hospitalisation or emergencyroomvisits and incur considerable medical expenses. 2. Opportunities (and pitfalls) of technology: The current health issue has numerous opportunities but also has the potential to cause a lot of issues if not properly addressed. Data are being used more and more in health. The difficulty is in managing this ocean of data. According to a Frontiers in ICT research, healthcare professionals and health systems were already producing about 80MB of data perpatientyear before the epidemic. In addition to information from electronic health records (EHRs), this data also contains information and detailssuchaddresses,demographics,claim and insurance information, payment history, and schedules. 3. Expensive medical bills: The exorbitant expense of healthcare is arguably the most serious issue facing our present healthcare system. More than 45% of American people say it is difficult to afford medical care,andmorethan 40% say they pay for treatment, according to a poll by the Kaiser Family Foundation. Healthcare costs are changing people's behaviour, with many avoiding a doctor when ill or skipping check-ups altogether. A quarter of Americans cannot afford the prescriptions they need and may skip doses or skip prescribed medications. Each of these behaviours can lead to serious health problems and, therefore, increased medical costs. WHAT IS THE SIGNIFICANCE OF AI AND THE FIELD OF ROBOTICS? Robots are becoming increasingly advanced both technologically and structurally. The primary focus of robotics today is on repairing and saving lives. For example, doctors use robot arms in hospitals to perform complicated surgeries without putting their patients at risk. AI is quickly becoming essential in many areasoflifeincludinghealthcare and cybersecurity. This is due to the fact that it saves lives, reduces costs and makes life easier. However, there are still many unknown with AI, which is why it is significant to consider the positives and negatives before implementing this technology in both healthcare and cybersecurity. AI has a lot of potential in healthcare; it can perform complex tasks
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 226 and can help doctors treat patients more effectively. For example, it can asset physicians in diagnosing and treating diseases and also assist them in performing triage and radiology procedures. Reinforcement learning programs help medical professionals save lives by performing life- saving surgeries on human beings. In addition, predictive models help medical professionals managepatients’ records and identify issues with patient care systems. Additionally, AI helps with patient counselling by assisting with diagnosis and providing psychological support to patients and essentially has the potential to revolutionize our healthcare system WHAT ARE THE CHALLENGES OF AI IN CYBERSECURITY AND HEALTHCARE AND HOW TO OVERCOME IT? AI is the term given to describe the advancement of computers to perform tasks that were once reserved for humans. It has the potential to revolutionizemanyaspects of our lives- from health and education to military and commercial sectors. However, it is also a source of considerable concern as it raises questions regarding ethics, safety, and accountability. AI is still in its infancy so there are still many challenges to overcome. For instance, AI is not very good at handling controversial or negative data, as it can have a conflictive effect on the system. It is also susceptible to adversarial behaviour since hackers can use AI for their own purposes by programming it against the systems they target. Many Cybersecurity experts believe that AI will be most beneficial in situations involving classified data, where security measures are necessary but impossible. The cybersecurity industry is getting bigger every year. As more and more people rely on technology in their daily lives, it's important to make sure these devices and computers are safe from hackers. There are somecybersecurityissuesthatareeasyto fix. For example, many people use the same password for their social media accounts and email girlfriend accounts. This makes it easier for hackers to steal passwords and use them to break into those accounts. They can then steal your personal information and use it to commit identity theft. Another problem with cybersecurity is that ordinary people are not fully aware of how to protect themselves. They are also unaware of the dangers of opening emails or attachments that appear to come from people they know. These emails may contain viruses thatcanharmyourdevice. It could also be a phishing scam that steals your personal information. The fundamental healthcare issue has a few other remedies as well. Collaboration between local, state, and federal governments, as well as healthcare professionals, is necessary to find answers to the problem of excessive healthcare expenditures. To address environmental variables and enhance access to healthcare in marginalized neighbourhoods, it is possible to employ housing, transportation, and collaborations with churches and non- profit health groups. To satisfy the demands of patients, healthcare managers might put up a several kinds of programs. Example, telemedicine can help patients who do not have access to transportation, as is the case in many rural places, yet internet connectivity is still anissue.Elderly home care is one of the other initiatives. a healthcare team that prioritises community involvement and patient care. WHAT IS THE RESEARCH GAP EXISTING IN AI IN CYBERSECURITY AND HEALTHCARE? Artificial Intelligence and Cybersecurity are two of the most important technologies today. CybersecurityandHealthcare are also two areas that are rapidly developing, expanding, and gaining more relevance in our daily lives. However, AI technologies have many flaws that need to be addressed- which is why more research is needed to make them more useful. Both areas are in a stage of development; therefore, they have many challenges to overcome before they can revolutionize our lives. AI has a lot of potential in Cybersecurity and Healthcare since it can help detect and prevent cybercrime when we take the field of Cybersecurity. And in healthcare, it can help diagnose a disease from its very early stage and also reduce the workload on the doctors as well. Currently, Cybercrime is mostly detected through human involvement, which is slow and error-prone.AIcanalsohelp with the investigation process by analysing data collected from various sources andidentifyingpotential threads.It can also help with countermeasures by developing mechanisms that stop attacks before they happen.Withthatbeingsaid, AI has the potential to become an invaluable tool for Cybersecurity and Healthcare when applied practically. WHAT IS THE FUTURE OF AI FROM A CYBERSECURITY AND HEALTHCARE PERSPECTIVE? Robotics and artificial intelligence have many exciting applications that will become clear once they're ready for use by the public. For now, these technologies are primarily used in scientific research or in niche applications by professionals only. However, there's no shortage of interest from amateurs who want to create their own robot companions. It's clear that these technologies have a huge future. AI has many applications- from natural language processing to pattern recognition and will change our lives in many years when we take a look at how it changes and is changing our daily lives from the perspective of cybersecurity and healthcare. It is very obvious that the AI has a very large and good future.
  • 5. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 227 2. METHODOLOGY Several research papers usedin this researchwereexplained in this part. Consequently, weprovide and clarify the current surveys in all the areas of this research including AI and Robotics, Cybersecurity, and Healthcare. A. APPLICATIONS OF AI IN CYBERSECURITY DEFENCE The AI model provides highlypowerfuldefensivecapabilities for cybersecurity protection that will help defend various systems against cyberattacks and support digital forensic investigations. Having said that,wehighlightafewoftheuses of AI in cybersecuritydefense.Additionally,weencouragethe reader of this research to look at these publications for additional information on AI's role in cybersecurity protection. i. AI for malware detection and classification: This term simply stands for “MaliciousSoftware”whichisactually dangerous in short. It is a document that contains programs or codes which is mostly delivered over a network [1] [2]. It is produced or planned to employ various methods, such as ransomware, spyware, viruses, trojans, and adware, to damage targetcomputer systems, mobile devices,andonline applications. [3] [4]. Several algorithms and techniques have been used to detect malware [5].DetectionofmalwareusingAItechniques can be done when a model is trained using a dataset that can help in classifying the type of malware [6]. ii. AI for network intrusion detection: Many programmers created and suggested network intrusion detection solutions. Ding et al. [7] presented a real-time anomaly detection technique andwassuccessfulinachieving high accuracy. Additionally, after conducting K-means clustering, Alom andTaha[8]attainedarespectableaccuracy of 91.86%. Chen et al [9] provided an example of how deep convolutional neural networks (DCNNs) are used to identify DDoS assaults. Some other researchers who worked on the same topic include Mirsky et al. [10], Biswas [11], Clements, et al. [12], and Xia et al. [13]. iii. AI for traffic identification and classification: At a time, several applicationsare flowinginanynetwork,andthe one and single most important phase in identifying and recognizing multiple classes is the use of network traffic classification. A researcher [14] utilized a deep learning model to distinguish the flowing of traffic in a network after diving it into 25 protocols, he was successfully able to get 100% and 91.74%, depending on the type of protocol. Another research [15] used a Convolutional Neural Network (CNN) model to distinguish the classes of traffic and also try to recognize the application category. iv. AI for spam detection: Spam emails, to put it simply, are any unwelcomeor virus-containing emails. Inadditionto acting as a detector of all those viruses, spam detection systems also work as a preventer of emails by stopping them from introducing viruses into one's inbox. One of the techniques that developers have suggested is an auto- encoder that functions and further distinguishes spam mail by Mi et al. [16], with a 95% accuracy rate. A different researcher created a machine-learning approach and algorithmic phishing email detectionsystem[17].Thereader of this paper can refer to the following works related to this by Aksu et al. [18], Yi et al. [19], and Benavides et al. [20]. v. AI for insider threat detection: A document that demonstratesandclearlyexplainshowtoexamineandassess a user's system logs using a DNN or RNN model, as well as how to find abnormalities that might lead toaninsiderthreat incident. Tuor et al. [21] described how to do this. vi. AI for digital forensics: AI technology become most significant in investigations nowadays and also improvesthe methodsand ways of detecting cybercrime.Thespecialistsof forensics found this very useful as it helps them in effectively and quickly find the actual source and cause of the problem, on the other hand, the use of AI in digital forensic saves a lot of money and time. Some machine-learning techniques or algorithms have been utilized to classify file fragments. For example, papers are written by Beebe et al. [22], Axelsson et al. [23], and Calhoun & Coles [24]. Another researcher [25] proposed a technique that works based on deep learning for file fragment classification. B. APPLICATIONS OF AI IN HEALTHCARE i. Disease Detection systems: One of the most significant tasks in healthcare is the detection of various diseases. it lessens the stress on doctors, because those systems may be replaced to run automatically instead of manually for various other duties. . Researchers have suggested a method in 2019 that might assist physicians in identifying and categorizing skin conditions, such as melanoma and eczema [26]. A machinelearning algorithm is used in detecting skin cancer where it differentiates healthy skin from diseased one and high accuracy was achieved [27]. Many systems for brain cancer classification have also been invented by developers which include an approach by Sha et al [28], they developed a system using deep Convolutional Neural Networks (DCNNs) to detect brain tumors after Magnetic Resonance Imaging (MRI) generated the high- quality images of the inside of the brain. The reader of this research can also go through these articles for disease detection systems: Ahmad et al. [29], Ahmad et al. [30], Shabbir et al [31], and Hussain et al [32]. ii. Test Analysis and Diagnosis: Because all those AI- based apps will haveahugeinfluenceoninterpretingmedical scans, including X-rays, MRI pictures, CT scans, and many more, it is getting simpler for physicians to simply comprehend the problemoftheirpatientswhenthereisanAI application. As the effort associated with scanninganalysisis
  • 6. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 228 lessened, medical physicians feel more at ease [33]. The AI- based approach will assist in realizing and comprehending whether any gene might cause cancer while evaluating biological data such as DNA and RNA [34]. The AI can help identify any disease risk or existence. The characteristics depend on outside factors [35]. It further helps in alerting people about any disease-infected area [36]. iii. Chatbots:Thesedays,hospitalsandotherclinicshold a number of websites, mobile applications, and web applications. These websites, mobile applications, and web applications feature chatbots that act to aid patients directly from where they are and try to learn more about their health issues [37]. Every time a patient enters a hospital, the first thing the medical staff does is screen the patient to learn about their beginning circumstances. In this situation, AI chatbots can take the role of these time-consuming procedures [38]. Additionally, chatbots may be used as interacting agents between language processing and speech recognition technology [39]. As a whole, majority of the modern healthcare institutions have these kinds of chatbots which help patients in different ways [40]. iv. Health Monitoring: When it comes to patient prevention through condition monitoring, this iscrucial.The monitoring system may occasionallybeabletokeepapatient in their present state when an illness is caught early by informing the doctors. Algorithms and AI approaches are used to assist it. A smart health monitoring system has been proposed by some researchers [41], the system is capable of keeping track of patients’ healthand it alsocontainsafeature that enables patients' families to access and check on their patient’s health status. Anandh [42] created a system that uses AI algorithms to provide body temperature. Papers written by Soppimath et al [43] and Srinivasan et al. [44] can be checked to get more health monitoring systems that were trained based on AI algorithms and techniques. v. Digital Consultation: The world is getting increasingly digital; thus, this is a fairly broad area. A digital consultation is just a video call between a doctor or other healthcare professionaland a patient made possible through a smartphone or online application. Through these tools, the patient and the doctor will communicate. Examples of the evolution of digital healthcare include patients' engagement in the development and higher expectations for patient access to healthcare [45, 46, 47]. Most primary care doctors can now operate from home, and in this scenario, digital consultation will undoubtedly occur [48, 49].Thisreadercan check [50] and [51] to get more ideas about digital consultations and their cost-effectiveness. C. DATA SOURCE The literature in this paper is made up of several research publications and articles from different sources. Fig.1 shows the pictorial or graphical representation of the data sources used in this research and their respective percentages. Additionally, wehave madea table of the databasesandtheir respective URLs that were allused in this researchwhichcan be seen in fig. 2. D. EXPLORATION CRITERIA As mentioned in the abstract that this research will focus more on the area of AI and robotics in cybersecurity and healthcare forthe past 6 years which is from2017to2022.In light of the foregoing, we gathered all the references and brought out the percentage of the papers used in this research for each and every respective year. The pictorial representation of the same is shown in fig. 3 where all the percentages are clearly stated. Fig.1. Research Papers from Data Sources. Fig.2. Database Engines and their URLs Fig.3. Percentage of Research Papers from 2017 to 2022.
  • 7. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 229 Cybersecurity is an ever-evolving field, and the systems developed in the past five years have been instrumental in helping protect individuals and organizations from cyber threats. In this article, we will take a look at some of the most important cybersecurity systems developed in the past five years in Table.1. Let's first examine how machine learning (ML) and artificial intelligence (AI) have evolved in the field of cybersecurity. Systems that can identify and respond to cyber threats in real-time have been developed using AI and ML. These technologiesarecapableofanalyzingvastvolumes of data to spot trends and abnormalities that can point to an impending attack. Systems that can recognize and react to harmful codehave also been created using AI and ML,aswell as systems that can detect and respond to phishing attacks. These are just a few of the many cybersecurity systems developed in the past six years. As the field of cybersecurity continues to evolve, new and improved systems will be developed to protect individuals and organizations from cyber threats. Table 1. Summary of AI-Based Cybersecurity Systems Developed in the past 6 years. [8] 2017 Cybersecurity network intrusion detection with unsupervised deep learning Attained a respectable accuracy of 91.86% Usability issues [53] 2017 Convolutional neural networks' ability to identify new assaults is evaluated. The CNN model obtained an 81.57% of accuracy rate. High dimensional data [54] 2017 developed a recurrent neural network-based intrusion detection system (RNNs) The RNN model has an 83.28% detection rate in the binary classification, according to the results. Personal Integrity [55] 2017 An innovative fuzziness-based semi-supervised learning strategy that uses unlabelled data with supervised learning algorithm assistance improves the classifier's performance for IDS. Obtained very high accuracy on the proposed algorithm. The accuracy of the J48, Naïve Bayes, NB tree, Random forests, Random tree, multi-layer perceptron, and Support Vector Machine (SVM) is lower than the proposed algorithm [57] 2018 A safe malware detection system using encryption Achieved 98.93% Efficiency [58] 2018 An Android malware family categorization has been proposed, along with a representative sample selection. FalDroid – 94.2% Usability [59] 2018 for unsupervised feature learning, a non-symmetric deep autoencoder (NDAE) has been suggested. a training time reduction of up to 98:81% and an improvement in accuracy of 5%. Huge amount of complex [60] 2019 a method to identify malware based on the incidence of opcodes The suggested method can identify the virus with about 100% accuracy. Less number of datasets.
  • 8. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 230 [61] 2019 Using data and APIs, to identify malware AUC 99.3% Privacy [56] 2019 examination of extracted characteristics from big-data sources in real time. True Positive Ratio, Precision, Recall and F1 > 99%, FPR < 0.1% Effectiveness [63] 2019 It was showed how to find malware payloads in a number of file types, including Portal Document File.pdf and Microsoft Document File.doc. The accuracy of finding ransomware was 91.7% and 94.1%, respectively. Limited incremental rate [64] 2019 Deep learning-based proposed method for virus detection using behaviour graphs Accuracy of 98.60% Unstructured [66] 2020 proposed a dynamic technique for detecting and predicting Windows malware Prediction – 0.997 FPR of 0.000 FNR of 0.007 Trust [67] 2020 suggested using a method called SourceFinder to locate malware source code repositories. According to the research, the suggested method locates malware repositories with 89% precision and 86% recall. Poor understanding of safety [68] 2021 They provide a novel approach for automatic hyperparameter optimization based on Bayesian optimization to produce the best possible DNN design. BO-GP obtained the highest accuracy scores, with 82.95% for the KDDTest+ dataset and 54.99% for the KDDTest-21 dataset. accuracy. Appropriateness [86] 2022 ML-based malware classification for Android devices using repacked app detection and removal Detection Accuracy of 98.2% Efficiency [87] 2022 Malware Threads Classification 98% Accuracy in detecting and classifying the malware threads Trust [62] 2022 Approaches in malware detection systems that rely on visualisation The Approach Achieved 100% Accuracy Poor and little amount of dataset to get high accuracy Table 2. Summary of Datasets, Samples, and Methodology used in the Past AI-Based Cybersecurity Systems Reference Title of Paper Methodology Datasets and Samples Used [86] AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems Nature Inspired Algorithm AndroZoo Dataset [55] Fuzziness-based semi-supervised learning approach for intrusion detection system Random forests, NB tree, J48, Naive Bayes, random tree, multi- layer perceptrons, and SVM (SVM) unlabelled samples assisted with a supervised learning algorithm. [54] Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks Binary classification (Normal, Anomaly) and five category classifications using the RNN-IDS model (Normal, DoS, R2L, U2R, and Probe). NSL-KDD dataset
  • 9. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 231 [87] Binary and Multi-Class Malware Threads Classification Naïve Bayes (NB) and Gaussian Discriminant Analysis (GDA) MaleVis Dataset [60] Detection of Advanced Malware by Machine Learning Techniques Machine Learning Techniques Kaggle Microsoft malware classification challenge dataset [68] Bayesian hyperparameter optimization for deep neural network-based network intrusion detection Deep Neural Network Algorithms NSL-KDD dataset [57] A secure encryption-based malware detection system Privacy-Preserving Naïve Bayes Classifier (PP – NBC) 4-Gram API Fragment Sequence [67] Source finder: Finding malware source code from publicly available repositories Machine Learning Techniques in detecting the Malware Not Identified [62] Disarming Visualization-based Approaches in Malware Detection Systems Visualization-based techniques Mallmg Dataset [8] Network intrusion detection for cybersecurity using unsupervised deep learning approaches K-means Clustering NSL-KDD dataset [61] ASSCA: API sequence and statistics features combined architecture for malware detection Dynamic behaviour Malicious samples from virus Share and VirusTotal, as well as samples from Windows 7 and Windows XP system exe files [63] A novel malware detection system based on machine learning and binary visualization Neural network and deep learning are used in the detection of the malware. Not mentioned [53] Intrusion Detection Using Convolutional Neural Networks for Representation Learning In testing the set, 17 extra attack kinds were added, and a new attack was also found. NSL-KDD dataset [66] A dynamic Windows malware detection and prediction method based contextual understanding of API call sequence Using Markov chain sequence to depict the link between API functions to represent malware and goodware Intelligent and Security Informatics Data sets Brazilian- malware-dataset [64] Malware detection based on deep learning of behaviour graphs Stacked AutoEncoders and the Behaviour-based Deep Learning Framework (BDLF) Malware samples from VX heaven [58] Android malware familial classification and representative sample selection via frequent subgraph analysis FallDroid Genome Project Dataset, Drebin Dataset, FallDroid – I, FallDroid - II [56] An investigative study on motifs extracted features on real time big- data signals Visualization and deep learning techniques were used The Virus Share community has 9 virus families, each with 1000 variants [59] A deep learning approach to network intrusion detection. By stacking the NDAEs, a layer- wise unsupervised representation learning method was produced. KDD Cup’99 and NSL-KDD datasets
  • 10. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 232 In the pastsixyears,healthcaresystemshaveundergonea dramatic transformation. Advances in technology, data analytics, and artificial intelligence have enabled the development of new and improved healthcare systems that are revolutionizing the way healthcare is delivered. These systems are designed to improve patient outcomes, reduce costs, and provide better access to care. Table.3 below presents some of the most significant healthcare systems developed in the past six years. These systems are designed to address a variety of healthcare needs, from patient monitoring and diagnosis to population health management. Each system is designed to provide a unique set of features and benefits to healthcare providers and patients alike. These healthcare systemsare just a few of the many that have been developed in the past five years. As technology continues to advance, healthcare providers will continue to develop new and improved systems to improve patient outcomes and reduce costs. Table 3. Summary of AI-Based Healthcare Systems Developed in the past 6 years. Reference Year Topic Addressed Performance Limitation [69] 2017 Human Skin Cancer Detection System 84% Predictive Value and 75% Sensitivity Unstructured Data [70] 2017 Skin Cancer Classification Using Deep Learning High performance achieved Lack of Elaboration [71] 2017 Review of Common AI Disease including, cancer, cardiology, and neurology Perfect Analysis in the Review Data Exchange and Safety [72] 2018 Detection of Onychomycosis and normal nails Sensitivity of 96.7% and a Specificity of 96.7% Too Much Load of Different Dataset [73] 2018 Skin Disease Identification 88% in detection Efficiency [74] 2018 Diagnosis of Skin Cancer Detection accuracy of 90% Less Flexible [75] 2019 Approach on Melanoma and other skin cancer types 99% of Accuracy in Classifying Skin Cancer Less amount of Data [65] 2019 Device Application for Skin Cancer Detection They Achieved an overall accuracy of 75.2% in detecting the Skin Cancer using the Application Detection of Only two Disease [76] 2019 Review of AI in Applications in India Detailed Review of the Topic Ethical Consideration [77] 2020 Brain Tumour/Cancer Detection CNN Architecture = 86% VGGNet = 97% Very less number of the images used [78] 2020 Skin Cancer Detection 97.9% Accuracy was achieved Interoperability [79] 2020 Classification of Skin Cancer Achieved an Accuracy of 94.5% Poor Documentation [80] 2021 Detection of Brain Cancer SVMs = 92.4% Five-layer Custom CNN = 97.2% Less Amount of dataset [81] 2021 Review of Common Healthcare Applications and Projects Clearly explained about the algorithms and techniques Poor Abstraction [82] 2021 The model can identify photographs that don't fit into the eight classifications that are 94.9 Accuracy Safety
  • 11. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 233 often utilised (Classified as unknown images) [83] 2022 Detection and Classification of Brain Tumour that are generated by MRI Overall accuracy of 98.87% in classification and detection Huge amount of complex [84] 2022 Chatbot System for Women’s Healthcare 96% for prediction of PCOS Restrictions (Only for Women) [85] 2022 Detection of Skin Cancer using different algorithms Accuracy of the proposed ensemble is 93.5% Trust [52] 2022 Classification of Skin Lesion Overall, of 98% Accuracy in Classifying Skin Lesion Privacy Table 4. Summary of Datasets, Samples, and Methodology used in the Past AI-Based Healthcare Systems Reference Title of Paper Methodology Dataset and Samples Used [84] Intelligent Medical Chatbot System for Women’s Healthcare Logistic Regression Algorithm, Machine Learning Algorithm, and KNN. DialogFlow [83] A Robust Approach for Brain Tumour Detection in Magnetic Resonance Images using Finetuned EfficientNet Deep Convolutional Neural Network Brats2015 Brain Tumour Dataset [76] Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey SWOT Analysis Review* [85] Skin Cancer Detection Using Combined Decision of Deep Learners SVM, Naïve Bays, and K- Nearest Neighbour ISIC Public Dataset [71] Artificial intelligence in healthcare: Past, present and future Support Vector/ Neural Networks Review* [78] Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection Convolutional Neural Networks (CNNs) DermIS [70] Dermatologist-level classification of skin cancer with deep neural networks Deep Learning Algorithms Not Specified [80] Brain Tumour Detection using Convolutional Neural Network SVMs, K-NN, multi-layer perceptron, Naive Bayes, and random forest algorithms HAM10000 [69] A machine learning algorithm for identifying atopic der-mastitis in adults from electronic health records Machine Learning Algorithms ISIC Dataset [52] Skin Lesion Classification System using a K- Nearest Neighbour Algorithm K-Nearest Neighbour Approach (KNN) and Convolutional Neural Network ISIC Public Dataset [81] Unbox the black-box for the medical explainable AI via multi-modal and multi Rule-based Decision Support System Review*
  • 12. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 234 centre data fusion: A minireview, two showcases and beyond [72] Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network Convolutional Neural Networks (CNNs) Not Identified [77] Brain Tumour Detection using Deep Learning Models Convolutional Neural Network and VGGNet HM1000 [82] Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning Deep Convolutional Neural Network in Addition to GoogleNet ISIC Dataset [79] Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method Neural and Fuzzy Approach ISIC Dataset [75] Integrated design of deep features fusion for localization and classification of skin cancer Otsu Algorithm, Alex and VGG-16 Model HAM10000 [65] An on-device inference app for skin cancer detection Convolutional Neural Network using Tensorflow ISIC Dataset [73] Automated skin disease identification using deep learning algorithm InceoptionV2, InceptionV3, MobileNet ISIC Dataset [74] Diagnosis of skin diseases using convolutional neural networks Convolutional Neural Networks ISIC Dataset The past six years have seen a dramatic shift in the way healthcare systems are developed and implemented. With the advent of new technologies and the increasing emphasis on patient-centered care, healthcare systems have become more efficient and effective. Table 4 highlighted some of the various healthcare systems developed in the past six years, their methodologies, and the datasets & samples used. ARTIFICIAL INTELLIGENCE AND ROBOTICS customer service, automate manufacturing processes, and develop autonomous vehicles. AI is also used to develop virtual assistants, such as Amazon Alexa and Google Assistant, which can understand natural language and respond to voice commands. AI is an ever-evolving field of research, and its potential applications are limitless. AI is significantly influencing cybersecurity and healthcare. AI is being utilized in cybersecurity to detect threats to the network more rapidly and accurately than ever before. AI- based systems are able to recognize harmful behavior, identify malicious actors,andrespondtothreatsinreal-time. This is helping to reduce the amount of time recognized takes to detect and respond to cyber threats, as well as reducing the cost of responding to them. In healthcare, AI is being used to diagnose and treatdiseases more accurately and quickly than ever before. Doctors may make more accurate diagnoses and administer better care when using AI-based systems, which can analyse vast The goal of the computer science field of artificial intelligence (AI) is to develop intelligent machines that can think and behave like humans. AI is used to develop computer systems that can solve complex problems, recognize patterns, and learn from experience. AI systems can be used to automate tasks, such as scheduling, data analysis, and decision-making. AI is also used to develop robots that can interact with humans and the environment. AI has applications in many industries, includinghealthcare, finance, and transportation. AI can be used to improve
  • 13. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 235 volumes of data to find patterns and trends inpatienthealth. In order to cut costs and increase efficiency, AI is now being utilized to automate administrative choreslikeappointment scheduling and insurance claim processing. AI appears to have a bright future in both cybersecurity and healthcare. More rapidly and precisely than ever, AI may be used to detect and address cyber threats. AI may also assist healthcare businesses better secure patient data by identifying possible security flaws. Healthcare practitioners might concentrate on more crucial activities by using AI to automate menial chores. AImayalsobeusedtoexamine vast volumes of data and find patterns and trends that can be utilized to enhance patient outcomes and treatment.Finally, AI can automate illness diagnosis and treatment, freeing up medical experts to work on more challenging situations. Characteristics of Artificial Intelligence 1. Automation: Artificial intelligence (AI) is able to do things like recognize patterns, make judgements, and solve problems that would typicallyneedhumanintelligence.Data analysis, natural language processing, and picture identification are a few examples of complicated jobs and processes that AI can automate. 2. Machine Learning: AI is capable of learning from its environment and experiences. Through machine learning algorithms, AI can learn from data and use it to improve its performance. On the other hand, Machine learning is a type of artificial intelligence (AI) that enables computers to learn without explicit programming. The goal of machine learning is to build computer programs that canaccessdata anduseit to learn for themselves. The learning procedure occurs with observations or data, such as examples, directexperience,or teaching, in order to uncover patterns within the information and enhance future judgements based on the examples we provide. The basic objective is to enable computers to learn independentlyofhumansandadapttheir behavior as a result. i. Supervised Learning: This kind of machinelearning algorithm makes predictions using labeled data. A labelled dataset with input data and the associated predicted output is used to train the algorithm. After that, the system makes predictions on fresh, unlabeled data using the labelled data. ii. Unsupervised Learning: This is a kind of machine learning method that generates predictions from unlabeled data. An unlabeled dataset, which consists of input data without any corresponding predicted output,isusedtotrain the algorithm. Afterward, the program makespredictions on fresh, unlabeled data using the unlabeled data. iii. Reinforcement Learning: This is an algorithm that uses rewards and punishments to learn. The algorithm is trained on an environment, which contains input data and the corresponding rewards or punishments. The algorithm then uses rewards and punishments to make decisions and take actions in the environment. Fig. 4. Types of Machine Learning 3. Natural Language Processing: AI can understand and process natural language, such as spoken words and written text. This allows AI to interact with humans in a more natural way. Similarly, the goal of the artificial intelligence (AI) branch of natural language processing (NLP) is to give computers the ability to comprehend, analyze, and modify human language. To analyze text, NLP algorithms are employed,allowingcomputerstounderstand the structure and meaning of thelanguageinorderto extract insights from text data. NLP can be used to automate tasks such as sentiment analysis, text classification, and entity extraction. 4. Adaptability: AI is capable of adapting to changing environments and conditions. AI can learn from its mistakes and use the data to improve its performance. On the other hand, Adaptability in AI refers to the ability of an AI system to adjust its behavior in response to changes in the environment or the user’s preferences. This allows the AI system to remain effective and efficient over time, even as the environment or user preferences change. This is important for AI systems that are used in dynamic environments, such as self-driving cars, where the environment is constantlychanging.Adaptabilityalsoallows AI systems to learn from their mistakes and improve their performance over time. 5. Automated Reasoning: AI can reason and draw conclusions from data. This allows AI to make decisions and solve problems without human intervention. On the other hand, automated reasoning is a subfield of artificial intelligence (AI) that focuses on using computers to reason logically about a given problem. Automated reasoning systems use algorithms to analyze a set of facts and rules to draw logical conclusions. Automated reasoning systemscan be used to solve problems in many different areas, such as
  • 14. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 236 mathematics, law, medicine, engineering, and philosophy. Automated reasoning can also be used to create new knowledge by combining existingfactsandrules.Automated reasoning systems are becoming increasingly important in the development of AI systems, as they can help to reduce the amount of manual labor required in problem-solving. 6. Autonomous Agents: AI can act independently and autonomously. This allows AI to take action without human input or direction. On the other way, Autonomous agents in AI are computer programs that can act independently in a given environment. They are able to perceive their environment, make decisions, and take actions to achieve their goals. Autonomous agents are used inmanyareasofAI, consisting computer vision, NLP, and machine learning. Autonomous agents can be used to automate tasks, such as scheduling, planning, and decision-making. They canalsobe used to interact with humans, such as in virtual assistants, catbots, and autonomous vehicles. Autonomous agents can be used to improve the efficiency of existing systems,aswell as to create entirely new systems. The Robotics Field Robotics is a field of engineering that focuses on the design, construction, and operation of robots. It involves the application of mechanical, electrical, and computer engineering principles to the design, manufacture, and operation of robots. Robotics is used in a variety of applications, including manufacturing,medical,military,and space exploration. Robotics engineering involvesthedesign, construction, and operation of robots. This includes the development of robotic systems, sensors, and actuators, as well as the integration of these components into a functioning robotic system. Robotics engineers must also consider the safety and reliability of the robot, as well as its ability to interact with its environment. Robotics engineers must also consider the application oftherobot.Thisincludes the development of algorithms for robot control, navigation, and manipulation. Roboticsengineersmustalsoconsiderthe ethical implications of their work, as robots are increasingly being used in a variety of applications, including those involving human interaction. Robotics engineering is a rapidly growing field, and the demand for qualified engineers is increasing. Robotics engineers are in high demand in a variety of industries, including manufacturing, medical, military, and space exploration. As the technology continues to advance, the demand for robotics engineers is expected to continue to grow. Robotics is becoming increasingly important in many areas of our lives. Robotics can be used to automate processes, reduce labor costs, and increase efficiency. Roboticscanalso be used to improve safety, reduce humanerror,andincrease accuracy. Robotics can also be used to explore new environments, such as space, and to perform dangerous tasks that would otherwise be too risky for humans. Additionally, robotics can be used to improve healthcare, such as through surgical robots and robotic prosthetics. Finally, robotics can be used to improve the quality oflifefor people with disabilities, by providing them with more independence and mobility. The future of robotics is an excitingone.Roboticstechnology is advancing rapidly, and it is expected to continue to do so in the coming years. Robotics will continue to be used in a variety of industries, from manufacturing to healthcare, and even in the home. As robots become more capable and more intelligent, they will be able to take on more complex tasks and interact with humans in more meaningful ways. In the future, robots may be able to perform tasks that are currently too difficult or dangerous for humans to do. They may also be able to provide companionship, help with household chores, and provide assistance to the elderly and disabled. As robotics technology continues to evolve, it is likely that robots will become an integral part of our lives. Different Types of Robotics Fig.5 Few Types of Robotics 1. Surgical Robotics: These are robotic systems that are used to assist in surgical procedures. They are designed to improve the accuracy and precision of the surgeon,and to reduce the risk of complications and errors. Surgical robots typically consist of a robotic arm attached to a console, which is operated by the surgeon. The robotic arm is equipped with various tools and instruments, such as a camera, scalpel, and forceps, which are used to perform the surgery. 2. Army Robotics: This is the use of robots and robotic technology in military applications. This includes the use of unmanned aerial vehicles (UAVs), unmanned ground vehicles(UGVs),unmanned underwatervehicles(UUVs),and other robotic systems for reconnaissance, surveillance, target acquisition, and other military missions.
  • 15. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 237 3. Security robotics is the use of robots to provide security services such as surveillance, access control, and perimeter protection. These robots are typically equipped with sensors, cameras, and other technologies to detect and respond to potential threats. They can be used to patrol areas, monitor access points, and detectintrusions.Theycan also be used to provide real-time information to security personnel, allowing them to quickly respond to any security incidents. 4. Service Robotics: Service robots are designed to interact with humans and provide assistance in a variety of tasks. Examples of service robots include vacuum cleaners, medical robots, and personal assistant robots. 5. AutonomousRobots:Autonomousrobotsare robots that are capable of making decisions and acting independently without any human input. Examples of autonomous robots include self-driving cars, unmanned aerial vehicles (UAVs), and search and rescue robots. 6. Industrial Robotics: Industrial robots are used in manufacturing and production processes to automate tasks such as welding, painting, assembly, and packaging. These robots are designed to be highly accurate and efficient, and are often used in hazardous environments. 7. Space Robotics: Space robots are designed to operate in space and are often used for exploration and research. Examples of space robots include the Mars rovers, space shuttles, and satellites. 8. Educational Robotics: Educational robots are designed to teach studentsaboutroboticsandprogramming. These robots are often used in classroomsandcanbeusedto teach students about robotics concepts such as sensors, motors, and programming languages. When we talk about how Robotics will impact the cybersecurity and healthcare sector, we will consider the following; Robotics is increasingly being used in the field of cybersecurity to help protect networks, systems, and data from malicious attacks. Robotics can be used to automate and streamline many of the tedious, manual tasksassociated with cybersecurity, such as vulnerability scanning, malware detection, and patch management. Robotics canalsobeused to detect and respond to threats in real-time, allowing for faster and more effective responses to cyberattacks. Robotics can also be used to help identify and mitigate potential threats before they become a problem. By using machine learning and artificial intelligence, robotics can analyze data and detect patterns that may indicate a potential threat. This can help organizations identify and address potential threats before they become a major problem. Robotics can also be used to help organizations better understand their security posture. By using robotics to analyze data and identify potential vulnerabilities, Organizations may enhance their security posturebyhaving a better understanding of it. In this case, it can be of help to organizations better protect their networks, systems, and data from malicious attacks. Finally, robotics can be used to help organizations comply with security regulations and best practices. Robotics can help organizations automate the process of ensuring that their networks, systems, and data are compliant with security regulations and best practices. This can help organizations reduce the risk of non-compliance andensure that their networks, systems, and data are secure. Similarly, the impact of the robotics in the healthcare sector may include the following; Robotics in healthcare is a rapidly growing field that has the potential to revolutionize the way healthcare is delivered. Robotics can be used to automate mundane tasks, reduce errors, and improve patient outcomes. Robotics can help in the accuracy improvement and speed of diagnosis and treatment. For example, Robotic systems can evaluate medical photos and find anomaliesfasterandmoreprecisely than humans. This can help to reduce the time it takes to diagnose and treat patients. Robotics can also help to reduce the risk of medical errors. Robotic systems can be programmedtofollowprotocols and procedures more accurately than humans, reducing the risk of mistakes. Robotics can also help to improve the safety of medical procedures. Robotic systemscanbeusedtoperform minimally invasive surgeries, reducing the risk of complications and improving patient outcomes. Robotics can also help toimprovetheefficiencyofhealthcare delivery. Robotic systems can be used to automatemundane tasks such as dispensing medications, reducing the amount of time it takes to complete these tasks and freeing up healthcare professionals to focus on more important tasks. Finally, robotics can help to improve access to healthcare. Robotic systems can be used to provide remote consultations and treatments, allowing patients to access healthcare from anywhere in the world. This can help to reduce the cost of healthcare and make it more accessible to people who may not have access to traditional healthcare services. 3. CONCLUSION In conclusion, AI and roboticsarerevolutionizingtheway we approach cybersecurity and healthcare systems. AI and robotics are providing us with more efficient and secure solutions for both industries, allowing us to better protect our data and improve healthcare outcomes. AI and robotics
  • 16. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 238 are also providing uswithnewopportunitiesforautomation, which can help reduce costs and increase efficiency. The potential for AI and robotics to revolutionize cybersecurity and healthcare systems is immense, and it is important to continue to explore and develop these technologies in order to maximize their potential. The integration of AI and robotics in cybersecurity and healthcare systems is a promising development that could revolutionizetheway we protect our data and provide medical care. AI and robotics have already been used to detect and respond to cyber threats, automate medical diagnosis, andassistwithsurgical procedures. As technology continues to evolve, it is likely that AI and robotics will become even more prevalent in the healthcare and cybersecurity industries. This could lead to improved security, increased efficiency, and better patient outcomes. Ultimately, the use of AI and robotics in healthcare and cybersecurity systems could have a positive impact on society as a whole. 4. FUTURE DIRECTION AI in cybersecurity and healthcare is expected tocontinue to grow in the future. AI-based systems can be used to detect and respond to cyber threats, as well as to detect and prevent healthcare fraud. AI may be employed to automatically analyse massive volumes of data to find patterns and anomalies that may indicate a security breach or healthcare fraud. AI can also be used to create more secure and efficient healthcare systems, such as by automating the process of scheduling appointments and managing patient records. In addition, AI can increase the precision and efficiency of medical diagnosis andtherapy, as well as to provide personalized healthcare services. Finally, AI can be used to create more secure and efficienthealthcare systems, such as by automating the process of scheduling appointments and managing patient records. 5. REFERENCES [1] M. Ahmad, “Malware in computer systems: Problems and solutions,” IJID (International Journal on Informatics for Development), vol. 9, p. 1, 04 2020. [2] N. Milosevic, “History of malware,” Digital forensics magazine, vol. 1, no. 16, pp. 58–66, Aug. 2013. [3] S. Gupta, “Types of malware and its analysis,” International Journal of Scientific Engineering Research, vol. 4, 2013. [Online]. Available: https://www.ijser.org/researchpaper/Types-of- Malware-andits-Analysis.pdf [4] Statista. A number of worldwide internet hosts in the domain name system (dns) from 1993 to 2019. [Online]. Available: https://www.statista.com/statistics/264473/number- ofinternet-hosts-in-the-domain-name-system/ [5] F. Kamoun, F. Iqbal, M. A. Esseghir, T. Baker, “AI and machine learning: A mixed blessing for cybersecurity”. [6] H.S. Anderson, A. Kharkar, B. Filar, B. Roth, “Evading machine learning malware detection,” Black Hat USA 2017, July 22-27, 2017. https://www.blackhat.com/docs/us-17/thursday/us- 17-Anderson-Bot-VsBot-Evading-Machine-Learning- Malware-Detection-wp.pdf, accessed November 6, 2018. [7] N. Ding, H. Ma, H. Gao, Y. Ma, and G.Tan, “Real-time anomaly detection based on long short-term memory and Gaussian Mixture Model,” Computers & Electrical Engineering, vol. 79, pp. 1-11, 2019. [8] M.Z. Alom, and T.M. Taha, “Networkintrusion detection for cybersecurity using unsupervised deep learning approaches,” In Proceedings of the 2017 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, pp. 63–69, 2017. [9] J. Chen, Y. Yang, K. Hu, H. Zheng, and Z. Wang, “DAD- MCNN: DDoS attack detection via multi-channel CNN,” In Proceedings of the 11th International Conference on Machine Learning and Computing: ICMLC '19, pp. 484-488, 2019. [10] Y. Mirsky, T. Doitshman, Y. Elovici, A. Shabtai, and A. Kitsune, “An ensemble of autoencoders for online network intrusion detection,” arXiv preprint arXiv:1802.09089, pp. 1-15, 2018. [11] S.K. Biswas, S. K, “Intrusion detection using machine learning: A comparison study,”International Journalof Pure and Applied Mathematics, vol. 118, no. 19, pp. 101-114, 2018. [12] J. Clements, Y. Yangy, A.A. Sharma, H. Huy, and Y. Lao, “Rallying adversarial techniquesagainstdeeplearning for network security, arXiv preprint arXiv:1903.11688v1, pp. 1-8, 2019 [13] S. Xia, M. Qiu, M. Liu, M. Zhong, and H. Zhao, “AI- enhanced automatic response system for resisting network threats,” In M. Qiu (Ed.): SmartCom 2019, LNCS 11910, pp. 221–230, 2019. [14] Z. Wang, “The Applications of Deep Learning on Traffic Identification”, BlackHat, 2015, https://www.blackhat.com/docs/us15/materials/us- 15-Wang-The-Applications-Of-Deep-Learning- OnTraffic-Identification-wp.pdf , accessed March 23, 2019. [15] M. Lotfollahi, R. Shirali, M.J. Siavoshani, and M. Saberian, “Deep packet: A novel approach for
  • 17. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 239 encrypted traffic classification using deep learning,” arXiv preprint arXiv:1709.02656, pp. 1-13, 2017. [16] G. Mi, Y. Gao, and Y. Tan, “Apply stackedauto-encoder to spam detection,” In Proceedings of the International Conference in Swarm Intelligence, Beijing, China, pp. 3–15, 2015. [17] M. Alauthman,M.Almomani, M.Alweshah,W. Omoush, and K. Alieyan, “Machine learning for phishing detection and mitigation,” In: Machine Learning for Computer and Cyber Security, B. Gupta, and Q.Z. Sheng, (eds), pp. 1-27, Taylor & Francis, 2019. [18] D. Aksu, Z. Turgut, S. Üstebay, and M.A. Aydin, “Phishing analysis of websites using classification techniques,” pp. 251–258. Springer, Singapore, 2019. [19] P. Yi, Y. Guan, F. Zou, Y. Yao, W. Wang, and T. Zhu, “Web phishing detection using a deep learning framework,” Wirel. Commun. Mob. Comput, pp. 1–9, 2018. [20] E. Benavides, W. Fuertes, S. Sanchez, and M. Sanchez, M.” Classification of phishing attack solutions by employing deep learning techniques: A systematic literature review,” in Á. Rocha and R. P. Pereira (eds.), Developments and Advances in Defense and Security, Smart Innovation, Systems and Technologies vol. 152, pp. 51-64, 2020. [21] A. Tuor, S. Kaplan, B. Hutchinson, N. Nicholsand, and S. Robinson, “Deep learning for unsupervised insider threat detection in structured cybersecurity data streams,” arXiv preprint arXiv:1710.00811, pp. 1-9, 2017. [22] N.L. Beebe, L.A. Maddox, L. Liu, and M. Sun, “Sceadan: Using concatenated n-gram vectors for improved file and data type classification,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 9, pp. 1519–1530, 2013. [23] S. Axelsson, “The normalised compression distance as a file fragment classifier,” Digital Investigation, vol. 7, no. 8, pp. S24–S31, 2010. [24] W.C. Calhoun, and D. Coles, “Predicting the types of file fragments,” Digital Investigation, vol. 5, pp. S14–S20, 2008. [25] Q. Chen, Q. Liao, Z. Jiang, J. Fang, S. Yiu, G. Xi, et al, “File fragment classification using grayscale image conversion and deep learning,” In Proceedings of the IEEE Symposium on Security and Privacy Workshops, pp. 140-147, 2018. [26] N. Soliman A. ALEnezi. “A Method of Skin Disease Detection Using Image Processing and Machine Learning” Procedia ComputerScience163(2019)85– 92. [27] Kritika Sujay R, Pooja Suresh Y, Omkar Narayan P, Dr. Swapna B.”Skin disease detection using machine learning” IJERT Vol. 9. Issue 3. 2021. [28] H. A. Shah, F. Saeed, S. Yun, J. -H. Park, A. Paul and J. -M. Kang, "A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet," in IEEE Access, vol. 10, pp. 65426- 65438, 2022, doi: 10.1109/ACCESS.2022.3184113. [29] A. H. Abdel-Gawad, L. A. Said and A. G. Radwan, "Optimized EdgeDetectionTechniqueforBrainTumor Detection in MR Images," in IEEE Access, vol. 8, pp. 136243-136259, 2020, doi: 10.1109/ACCESS.2020.3009898. [30] A. S. Musallam, A. S. Sherif and M. K. Hussein, "A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images," in IEEE Access, vol. 10, pp. 2775-2782, 2022, doi: 10.1109/ACCESS.2022.3140289. [31] M. Rizwan, A. Shabbir, A. R. Javed, M. Shabbir, T. Baker and D. Al-Jumeily Obe,"Brain TumorandGliomaGrade Classification Using Gaussian Convolutional Neural Network," in IEEE Access, vol. 10, pp. 29731-29740, 2022, doi: 10.1109/ACCESS.2022.3153108. [32] Mahbub Hussain, Jordan J. Bird, and Diego R. Faria “A Study on CNN Transfer Learning for Image Classification”Contributions Presentedatthe18thUK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. January 2019 DOI: 10.1007/978-3-319-97982-3_1 [33] A. Kumar and S. Joshi “Applications of AI in Healthcare Sector for Enhancement of Medical Decision Making and Quality of Services,” in 022 International Conference on Decision Aid SciencesandApplications (DASA)|978-1-6654-9501-1/22/$31.00©2022IEEE | DOI: 10.1109/ DASA54658.2022.9765041. [34] “Understanding Cancer using Machine Learning | by Pier Paolo Ippolito | Towards Data Science.” https://towardsdatascience.com/understanding- cancerusing-machine-learning-84087258ee18 (accessed Aug. 14, 2021). [35] A. Maharana and E. O. Nsoesie, “Use of Deep Learning to Examine the Association of the Built Environment With PrevalenceofNeighborhoodAdultObesity,”JAMA
  • 18. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 240 Netw. Open, vol. 1, no. 4, pp. e181535–e181535, Aug. 2018,doi:10.1001/JAMANETWORKOPEN.2018.1535. [36] P. Kostkova, “A roadmap to integrated digital public health surveillance,” Proc. 22nd Int. Conf. World Wide Web - WWW ’13 Companion, pp. 687–694, 2013, doi: 10.1145/2487788.2488024. [37] M. Bryant, “Hospitals turn to chatbots, AI for care | Healthcare Dive,” Healtcare Dive, 2018. https://www.healthcaredive.com/news/chatbots- aihealthcare/516047/ (accessed Aug. 14, 2021). [38] A. Kumar and S. Joshi “Applications of AI inHealthcare Sector for Enhancement of Medical Decision Making and Quality of Services,” in 022 International Conference on Decision Aid SciencesandApplications (DASA)|978-1-6654-9501-1/22/$31.00©2022IEEE | DOI: 10.1109/ DASA54658.2022.9765041. [39] A. Jouman Hajjar, “6 Chatbot Applications / Use Cases in Healthcare in 2021,” AI Multiple, 2021. https://research.aimultiple.com/chatbot-healthcare/ (accessed Aug. 14, 2021). [40] K. Kalinin, “Healthcare Chatbots: Role of AI, Benefits, Future, Use Cases, Development.” https://topflightapps.com/ideas/chatbots-in- healthcare/ (accessed Feb. 16, 2022). [41] A. Mihat, N. Mohd Saad, E. Shair, A. Aslam and R. Abdul Rahim, "SMART HEALTH MONITORING SYSTEM UTILIZING INTERNET OF THINGS (IoT) AND ARDUINO", Asian Journal Of Medical Technology, vol. 2, no. 1, pp. 35-48, 2022. Available: 10.32896/ajmedtech.v2n1.35-48 [42] R. Anandh and G. Indirani, "Real Time Health Monitoring System Using Arduino with Cloud Technology", Asian Journal of Computer Science and Technology, vol. 7, no. 1, pp. 29-32, 2018. Available: 10.51983/ajcst-2018.7.s1.1810. [43] V. Soppimath, A. Jogul, S. Kolachal and P. Baligar, "Human Health Monitoring System Using IoT and Cloud Technology - Review", International Journal of Advanced Science and Engineering,vol.5, no.2,p.924, 2018. Available: 10.29294/ijase.5.2.2018.924-930. [44] C. Srinivasan, G. Charan and P. Sai Babu, "An IoT based SMART patient health monitoringsystem",Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 3, p. 1657, 2020. Available: 10.11591/ijeecs.v18.i3.pp1657-1664. [45] Regeringskansliet. Vision e-hälsa 2025 – gemensamma utgånspunkter för digitalisering i socialtjänst och hälso – och sjukvård. Socialdepartementet och SKL; 2016. https://www.regeringen.se/499354/contentassets/7 9df147f5 b194554bf401dd88e89b791/vision-e- halsa-2025-overenskommelse.pdf Accessed 12 June 2020. [46] Baird B, Charles A, Honeyman M, Maguire D, Das P. Understanding pressures in general practice. London: King’s Fund; 2016 [47] Greenhalgh T, Shaw S, Wherton J, Vijayaraghavan S, Morris J, Bhattacharya S, et al. Real-world implementation of video outpatient consultations at macro, meso, and micro levels: mixed-method study. J Med Internet Res. 2018;20:e150. [48] Chen J, Lan YC, Chang YW, ChangPY.Exploring doctors’ willingness to provide online counseling services: the roles of motivations and costs. Int J Environ Res Public Health. 2019;17:110. [49] Allen TD, Golden TD, Shockley KM. How effective is telecommuting? Assessing the status of our scientific findings. Psychol Sci Public Interest. 2015;16:40–68 [50] SKR. Statistik om hälso – och sjukvård samt regional utveckling 2018; 2018. https://skr.se/ekonomijuridikstatistik/statistik/ ekonomiochverksamhetsstatistik.1342.htmlAccessed 12 June 2020. [51] Ekman B. Cost analysis of a digitalhealthcaremodelin Sweden. Pharmacoecon Open. 2018;2:347–54. [52] M. Q. Hatem “Skin Lesion Classification SystemUsinga K-Nearest Neighbour Algorithm”, Hatem Visual Computing for Industry, Biomedicine, and Art (2022) https://doi.org/10.1186/s42492-022-00103-6 [53] Z. Li, et al. Intrusion Detection Using Convolutional Neural Networks for Representation Learning. In International Conference on Neural Information Processing (pp. 858-866). Springer, Cham,November 2017. [54] C. Yin et al. Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks. IEEE Access, 5, 21954-21961. [55] R. Ashfaq, et al. Fuzziness based semi-supervised learning approach for intrusion detection system. Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484-497, 2017.
  • 19. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 241 [56] S. T. Ahmed and K.K Patil, “An Investigative study on motifs extracted features opn real-time big-data signals”, in Proceedings of the 2016 International Conference on Emerging Technological Trends (ICETT), Kollam, India, IEEE, 2016, pp. 1-4. Doi: 10.1109/ICETT.2016.7873721 [57] Z. Lin, X. Fei, S. Yi, M. Yan, X. Cong-Cong and H. Jun, “A secure encryption-based malware detection system.” KSII TransactiononInternetandInformationSystems (TIIS), Vol. 12, no. 4, April 2018, pp.1799-1818. Doi: 10.3837/tiis.2018.04.022. [58] M. Fan, J. Liu, X. Luo, K. Chen, Z. Tian, Q. Zheng, and T. Liu, “Android malware familial classification and representativesampleselectionvia frequentanalysis” IEEE Transaction on Information Forensics and Security, Vol. 13, No. 8 August 2018, pp. 1890-1905, doi: 10.1109/TIFS.2018.2806891. [59] N. Shone et al. A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50, 2018. [60] S. Sharma, R. Challa, and S. Sahay, Detection of Advanced Malware by Machine Learning Techniques: Proceedings of SoCTA 2017, 01 2019, pp. 333–342. [61] L.Xiaofeng, J. Fangshuo, Z. Xiao, Y. Shengwei, S. Jing and P. Lio “ASSCA: API sequence and statistics features combined architecture for malware detection”, Computer Networks, Vol. 157, July 2019, pp. 99-111, doi: 10.1016/j.comnet.2019.04.007. [62] L. S. Fasci, M. Fisichelle, G. Lax, and C. Qian “Disarming Visualization-basedApproachesinMalwareDetection Systems” in Computers & Security · December 2022 DOI: 10.1016/j.cose.2022.103062 [63] I. Baptista, S. Shiaeles, and N. Kolokotronis, “A novel malware detection system based on machine learning and binary visualization,” 05 2019, pp. 1–6. [64] F. Xiao, Z. Lin, Y. Sun and Y. Ma, “Malware detection based on deep learning of behaviour graphs”, Mathematical Problems in Engineering, Vol.2019, February 2019, pp. 1-10, doi: 10.1155/2019/8195395. [65] Dai XF, Spasić I, Meyer B, Chapman S,AndresF(2019) Machine learning on mobile: An on-device inference app for skin cancer detection. In: Abstracts of the 4th international conference on fog and mobile edge computing, IEEE, Rome, 10-13 June 2019. https://doi.org/10.1109/FMEC.2019.8795362 [66] E. Amer and I. Zelinka, “ A dynamic windowsmalware detection and prediction method based oncontextual understanding of API call sequence”, Computers and Security, Vol. 92, February 2020, pp. 1-5, doi: 10.1016/j.cose.2020.101760. [67] M. O. F. Rokon, R. Islam, A. Darki, E. Papalexakis,andM. Faloutsos, “Sourcefinder:Finding malwaresource-code from publicly available repositories,” in RAID, 2020. [68] M. Mohammad, S. Hossain, H. Hisham, H. F. Md Jobair, V. Maria, K. Md Abdullah, A. R. Mohammad, A. Muhaiminul I., C. Alfredo, and W. Fan, “Bayesian hyperparameter optimization for deep neural network-based network intrusion detection,” IEEE International Conference on Big Data, 2021. [69] Gustafson E, Pacheco J, Wehbe F, Silverberg J, ThompsonW. A machine learning algorithm for identifying atopic der-matitis in adults from electronic health records. 2017 IEEEInternationalConferenceon Healthcare Informatics (ICHI).2017;2017:83---90. [70] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM,et al. Dermatologist-level classification of skin cancer withdeep neural networks. Nature. 2017;5427639:115---8 [71] F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang, ‘‘Artificial intelligence in healthcare: Past, present and future,’’ Stroke Vascular Neurol., vol. 2, no. 4, pp. 230–243, 2017, doi: 10.1136/svn-2017-000101. [72] Han SS, Park GH, Lim W, Kim MS, Na JI, Park I, et al. Deep neuralnetworks show an equivalent and often superior performanceto dermatologists in onychomycosis diagnosis: automaticconstruction of onychomycosis datasets by region-based con- volutional deep neural network. PLoS One. 2018;13:e0191493 [73] Patnaik SK, Sidhu MS, Gehlot Y, Sharma B, Muthu P (2018) Automated skin disease identification using deep learning algorithm. Biomed Pharmacol J11(3):1429–1436. https://doi.org/10.13005/bpj/1507 [74] Rathod J, Waghmode V, Sodha A, Bhavathankar P (2018) Diagnosis of Skin diseasesusingconvolutional neural networks. In: Abstracts of the2nd International lconference on electronics, communication and aerospace technology. Coimbatore: IEEE. https://doi.org/10.1109/ICECA.2018.8474593 [75] Amin J, Sharif A, Gul N, Anjum MA, Nisar MW, Azam F et al (2020) Integrated design of deep features fusion
  • 20. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 242 for localization and classification of skin cancer. Pattern Recogn Lett 131:63–70. https://doi.org/10.1016/j.pa trec.2019.11.042 [76] A. Mahajan, T. Vaidya, A. Gupta, S. Rane, and S. Gupta, ‘‘Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey,’’ Cancer Res., Statist., Treatment, vol. 2, no. 2, p. 182, 2019, doi: 10.4103/crst.crst_50_19 [77] S. Grampurohit, V. Shalavadi, V. R. Dhotargavi, M. Kudari, and S. Jolad, ``Brain tumor detection using deep learning models,'' in Proc. IEEE India CouncilInt. Subsections Conf. (INDISCON), Oct. 2020, pp.129_134. [78] R. Ashraf, S. Afzal, A. Rehman, S. Gul, J. Baber, M. Bakhtyar, I. Mehmood, O. Song, and M. Maqsood, “Region-of-Interest Based TransferLearningAssisted Framework for Skin Cancer Detection”, IEEE ACCESS, Digital Object Identifier 10.1109/ACCESS.2020.3014701 [79] Balaji MSP, Saravanan S, Chandrasekar M, Rajkumar G, Kamalraj S (2021) Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method. J Ambient Intell Human Comput 12(7):7181–7194. https://doi.org/10.1007/s12652-020-02394-0 [80] G. Kumar, P. Kumar, and D. Kumar, ``Brain tumor detection using convolutional neural network,'' in Proc. IEEE Int. Conf. Mobile Netw. Wireless Commun. (ICMNWC), Dec. 2021, pp. 1_6. [81] G. Yang, Q. Ye, and J. Xia, ‘‘Unbox the black-box for the medical explainable AI via multi-modal and multi- centre data fusion: A minireview, two showcases and beyond,’’ 2021, arXiv:2102.01998. [82] Kassem MA, Hosny KM, Fouad MM (2020) Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network andtransfer learning. IEEE Access 8:114822–114832. https://doi.org/10.1109/ACCESS.2020.3003890 [83] H. A. Shah, F. Saeed, S. Yun, J. Park, A. Paul, and J. Kang, “A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet”, IEEE ACCESS Digital Object Identifier 10.1109/ACCESS.2022.3184113 [84] S. Polekar, S. Wakde, M. Pandare, P. Shingane, “Intelligent Medical Chatbot System For Women’s Healthcare” ITM Web of Conference 44, 03020 920 (2022) https://doi.org/10.1051/itmconf/20224403020. [85] A. Imran, A. Nasir, M. Bilal, G. Sun, A. Alzahrani, and A. Almuhameed, “Skin Cancer DetectionUsingCombined Decision of Deep Learners”, IEEE ACCESS, Digital Object Identifier 10.1109/ACCESS.2022.3220329. [86] H. Rafiq, N. Aslam, M. Aleem, B. Issac, and R. H. Randhawa “AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Androidsystems”,ScientificReports | (2022) 12:19534|https://doi.org/10.1038/s41598- 022-23766-w [87] Ahmed, I.T. Jamil, N.; Din, M.M. Hammad, B.T. Binary and Multi-Class Malware Threads Classification. Appl. Sci. 2022, 12, 12528. https://doi.org/10.3390/app122412528