SMART METER SECURITY ISSUES: A REVIEW PAPER

International Journal of Network Security & Its Applications (IJNSA) Vol.17, No.4, July 2025
DOI: 10.5121/ijnsa.2025.17401 1
SMART METER SECURITY ISSUES: A REVIEW PAPER
Osama Alshannaq 1
, Mohd Rizuan Baharon 1
, Shekh Faisal Abdul Latip 1
,
Hairol Nizam Mohd Shah 2
and Áine MacDermott 3
1
Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka,
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
2
Fakulti Teknologi & Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka,
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
3
School of Computer Science and Mathematics, Liverpool John Moores University,
Liverpool, L3 3AF, United Kingdom.
ABSTRACT
In recent decades, conventional electric power systems have seen escalating issues due to rising electrical
consumption, leading to voltage instability, recurrent blackouts, and heightened carbon emissions. These
challenges highlight the pressing necessity for a more efficient and sustainable energy infrastructure. The
smart grid has emerged as a disruptive solution, providing improved energy distribution, real-time
monitoring, and facilitating renewable integration. Central to this evolution are smart meters, which are
essential elements of the Advanced Metering Infrastructure (AMI), facilitating precise energy monitoring,
bidirectional connectivity, and remote oversight within smart households and grids. Nonetheless, despite
their functionalities, smart meters provide potential security threats, including susceptibility to
cyberattacks and physical interference. This review article seeks to examine the fundamental
characteristics and functionalities of smart meters, identify significant security and implementation
difficulties, and emphasise their role within the larger smart grid ecosystem. This paper conducts a
thorough analysis of existing literature to analyse the communication technologies utilized, the possible
threat landscape, and the significance of strong security frameworks. The results underscore the necessity
for secure communication protocols, sophisticated encryption, and physical protections to guarantee the
reliability and integrity of smart meter implementations in contemporary power systems.
KEYWORDS
Smart grid; Secure smart meter; Attacks on data; network attacks; Physical hardware attacks.
1. INTRODUCTION
The global electricity sector is undergoing a radical transformation, with smart grids replacing
aging power grids. The need to reduce carbon emissions, meet growing energy demands, and
make electricity distribution more reliable and efficient are the primary drivers of this
transformation. Smart meters are a key part of advanced metering infrastructure (AMI) and are
essential to this transformation. They enable real-time monitoring, two-way communication, and
remote management of energy consumption [1][2]. Smart meters have changed the way we use
and manage energy by facilitating demand regulation, dynamic pricing, and communication with
home energy management systems (HEMS). While smart meters offer certain operational
advantages, they also pose serious privacy and security threats that must be addressed to maintain
the stability and reliability of smart grid systems [3], [4]. Smart meter security can be divided into
three main categories: data security, network security, and physical security. Data security is the
process of ensuring the availability, privacy, and accuracy of the data collected and transmitted by
smart meters. Because these devices often handle sensitive user data, such as complex

2
consumption patterns [5], they are easy targets for data theft, tampering, or the creation of
unauthorized profiles.
Network security aims to protect the communication channels used by smart meters, which often
rely on radio frequency (RF) or power line communications (PLC). Many different types of
attacks can occur on these channels, such as man-in-the-middle (MITM) attacks, replay attacks,
spoofing, and eavesdropping [6], [7]. Smart meters are typically placed in locations easily
accessible to the public, making them easy to tamper with, access unauthorized devices, or
replace. For this reason, physical security is critical. People with malicious intent can exploit
physical access to manipulate the meter's operation or install malware [8].
Alongside these issues, there is growing concern about user privacy. Smart meters collect
extensive information about how people use their devices, how often they are at home, and how
they behave there. If this information is shared without authorization, it could be monitored or
exploited by third parties [9], [10]. Given these complex risks, protecting smart meters is not just
a technological need, but also an ethical imperative. To address these threats, recent research has
proposed a number of intrusion detection systems, lightweight encryption methods, and privacy-
protecting protocols. However, challenges remain, particularly in finding the right balance
between robust security and the limited processing power of smart meters [11], [12]. This review
paper aims to provide a focused look at the privacy and security issues that arise with smart
meters. It begins by explaining how smart meters work and their purpose in the smart grid. It then
addresses three types of threats and vulnerabilities: physical, network, and data. This is achieved
with the help of real-world case studies and research findings. Finally, it explores new approaches
and areas of research that could lead to reliable smart metering systems that respect individual
privacy.
However, underscores the dual characteristics of this technology. While smart meters provide
operational benefits, they also have significant vulnerabilities. These vulnerabilities are
categorised into three main areas: data security, network security, and physical security. Each of
these domains is specific to threats such as data breaches, channel attacks, and physical
tampering. Another key issue raised is privacy, especially in relation to the extensive user data
collected by smart meters. The threat of unauthorised profiling or surveillance presents both
ethical and technical challenges. Therefore, securing smart meters is framed not only as a
technical requirement but also as an ethical responsibility.
Unlike prior reviews that often focus on a single aspect of smart meter security, such as
encryption methods, communication protocols, or individual threat categories, this paper offers a
unified and multidimensional assessment of security risks in smart metering systems. It
contributes a comparative analysis across network, data, and physical attack vectors, and maps
these to corresponding security properties such as confidentiality, integrity, and authentication.
Furthermore, this review critically evaluates the suitability of proposed solutions in the context of
real-world constraints, such as computational limitations and deployment scalability. It also
uniquely emphasizes the intersection of security and privacy, a dimension frequently
underrepresented in earlier surveys. By consolidating these perspectives, the paper provides a
comprehensive reference point for researchers and practitioners seeking holistic and
implementable smart meter security strategies.
By outlining the aim of this paper: to provide an organized review of current threats and
solutions, supported by case studies and emerging research. It paves the way for an in-depth
discussion on the development of secure and privacy-preserving smart metering systems that are
compatible with the resource constraints of these devices.The remaining sectionsare described as

3
follows. Section 2 explains the Literature Review, Section 3 Architecture of Smart Grid, Section
4 Smart Meter Security Issues, and Section 5 the Conclusion.
2. LITERATURE REVIEW
Several studies have addressed threats to smart meters. Since many electric utilities plan to
integrate the smart grid, installing smart meters in every household is crucial. These devices
enable remote load monitoring, transparent communication between consumers and providers,
and even remote disconnection of power. We categorize commonly reported security
vulnerabilities to inform strategies
2.1. Smart Meter Advantages
Smart meters offer multiple benefits from three perspectives as illustrated in Figure 1.
Figure 1. Beneficiary categories of smart meters
2.1.1. Advantage Experienced by Customers through the Use of Smart Meters
Cost Savings: Consumers can track real-time usage, shifting heavy-load tasks to off-peak hours,
and reducing electricity bills[13], [14], [15].
More Accurate Billing: Detailed daily or hourly reports eliminate estimation errors.
Improved Supply Quality:Faster diagnosis and resolution of maintenance issues lead to fewer
power interruptions [16], [17]. This is possible because the smart meter allows for the remote
collection of data and minor maintenance, eliminating the need to wait for scheduled
maintenance. Consequently, several challenges can be addressed more promptly.
2.1.2. Advantages Realized by Service Providers through the Utilization of Smart Meters
Automated Meter Reading: Reduces manual labor, thereby saving costs.
Better Demand Management: Smart meters help track overall energy consumption in real time,
aiding faster fault detection.
Enhances Customer Engagement: More precise consumption data can guide users toward
efficient energy use. [13], [14], [15],[16].

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2.1.3. Benefits Experienced by the Government through the Implementation of Smart
Meters
Environmental Gains: Lower consumption means reduced CO₂ emissions[13][15]
Economic Stimulation: Large-scale deployments create opportunities in meter production, IT
infrastructure, and other sectors[16].
2.2. Drawbacks of Existing Studies and Limitations of Prior Techniques
Although numerous studies have proposed security improvements for smart meters, most suffer
from critical shortcomings that limit their practical application in real-world smart grid
environments. For example, schemes such as "Secure Query Processing of Smart Grid Data
Using Searchable Symmetric Encryption" and "A Practical Searchable Symmetric Encryption
Scheme for Smart Grid Data" rely heavily on centralised key management and impose
computational burdens unsuitable for resource-constrained devices. Other schemes, like
"Searchable Multi-Keyword Encryption for Smart Grid Edge Computing," allow for flexible
searching but come with high communication costs and complicated key distribution, assuming
that edge servers can be trusted, something that's not realistic because edge nodes can be
physically attacked. Furthermore, many existing technologies fail to adequately preserve user
privacy, often leaking access patterns or metadata, and rarely incorporate privacy-preserving
methods such as ORAM due to their complexity. Real-time performance is another common
limitation, as many proposed models exhibit high latency and are only evaluated through
simulations without hardware validation. Furthermore, these solutions typically lack
standardisation and compatibility with existing AMI infrastructure, complicating their
deployment across diverse utility systems. Finally, physical and firmware security vulnerabilities,
including hardware tampering and malware injection, as well as the risk of insider threats, are
often overlooked, resulting in incomplete security models that must be addressed in future
research [86].
The latest comparison of searchable symmetric encryption (SSE) systems for smart grid data
reveals major issues with performance, scalability, and privacy. The system suggested by Wang
et al. in "Secure Query Processing of Smart Grid Data Using Searchable Symmetric Encryption"
aims to keep queries private and lower computing costs by using keyword matching, but it has
trouble handling changes to data, like updating or deleting records. In contrast, the solution
presented by Zhang et al. in "A Practical Searchable Symmetric Encryption System for Smart
Grid Data" enhances ease of use by supporting simple operations designed for smart meter
constraints; however, it still relies on centralised key distribution and lacks resistance to the
statistical leakage of query patterns. Meanwhile, Liu et al.'s approach in "Searchable Multi-
Keyword Encryption for Smart Grid Edge Computing" supports complex multi-keyword queries
and enhances the flexibility of edge processing. However, this increased expressiveness results in
increased storage and communication costs, making it less suitable for large-scale AMI
deployments. In general, although these systems contribute significantly to securing data access
in smart grids, they are hampered by issues such as poor forward privacy, poor update efficiency,
reliance on trusted servers, and partial protection against inference attacks. So, future efforts
should focus on creating simpler, privacy-protecting SSE models that can handle changing
queries, cost less to run, and work well for real-time smart meter uses [87].
When comparing the reviewed schemes, notable methodological differences emerge. For
example, solutions like Wang et al.'s keyword-matching SSE are lightweight but limited in
dynamic query handling, while Liu et al.'s multi-keyword scheme improves flexibility at the cost
of increased storage and communication overhead. In terms of effectiveness, centralised key

5
management approaches may simplify control but present a single point of failure, reducing
resilience to targeted attacks. On the other hand, distributed models often assume a high-trust
edge environment, which may not hold in real-world deployments. Furthermore, while some
methods prioritise performance through hardware-efficient encryption, they tend to sacrifice
privacy protections such as forward secrecy or access pattern confidentiality. This comparative
perspective reveals a trade-off landscape where achieving security, privacy, scalability, and
efficiency simultaneously remains an unresolved challenge.
2.3. Literature Gap
The shift to smart grids and the widespread deployment of smart meters significantly enhances
energy management capabilities. However, existing literature predominantly addresses smart
meter security threats individually and lacks a comprehensive analysis integrating network, data,
and physical security dimensions. Furthermore, previous studies frequently overlook the practical
constraints of resource-limited smart meter devices and fail to adequately discuss the ethical and
privacy implications associated with large-scale deployments.
This review addresses these gaps by providing an integrated and holistic evaluation of smart
meter security threats, emphasizing the interconnectivity of these vulnerabilities. Additionally, it
highlights the importance of developing scalable, lightweight, and robust security solutions
tailored to resource-constrained environments, identifying new directions for future research.
3. ARCHITECTURE OF SMART GRID
Figure 2 and 3 illustrate the typical smart grid metering system that uses sensors, meters, and
actuators connected to a central station. Customers and relevant agencies can access the collected
data. The central head-end system (HES) retrieves data from terminal nodes (smart meters,
gateways, data concentrators), relaying information via wired or wireless connections[17] , [18],
[19].
Figure 2. Architecture of Smart Grid[17]

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Figure 3.Architecture of a Smart Meter[18]
4. SMART METER SECURITY ISSUES
4.1.Network Attacks
Wireless communication (e.g., ZigBee, RF) can be exploited by attackers through eavesdropping,
replay attacks, and network interruptions [37].In wired setups, open ports may also expose
vulnerabilities where malicious data or code is injected. Ensuring secure routing is a common
protective measure [20]– [24], Figure 4 highlights the security issues on smart meter.
Figure 4. Research structure and security issues on Smart Meter
Many studies, as outlined in Table 1, have emphasized the growing security risks associated with
network attacks across various digital environments, particularly in software-defined networks
(SDNs), big data systems, and energy infrastructures. SDNs are especially vulnerable due to their
centralized control mechanisms, which can be exploited for denial-of-service and spoofing
attacks. In big data environments, attackers can misuse advanced analytics to extract sensitive
information and conduct targeted intrusions, raising serious concerns about privacy and data
integrity. Some researchers have proposed proactive frameworks that utilize behavioural analytics
and threat modelling to monitor security states and predict potential attacks. Additionally, real-
world cyber incidents have shown that threats now extend across domains, impacting both critical

7
energy infrastructures and social media platforms, highlighting the convergence of cyber risks in
interconnected digital ecosystems.
Table 1. Security issues (network attacks) with a smart meter.
Author Security issue Implemented Solution Disadvantages and weaknesses
(Karmakar et
al.,2019)
Attacks in SDN Policy-based security
application framework.
Lacks real-world efficacy validation.
(Liu et al.,
2023)
Pre-emptive
overflow attacks
on SDN
POA Guard: specialized
defence mechanism.
It addresses POA, but the scalability
of large networks is unclear.
(Li and Min,
2019)
Big data-driven
attacks
Data fusion tracking
recognition.
Privacy compliance must be ensured
(Zhan et al.,
2020)
security state and
predict an attack
Semi-CRF-based event
extraction.
Limited generalizability beyond
tested datasets.
Kumar et
al.,2019)
Real cyberattacks
on SM networks
Threat taxonomy &
classification.
Relies on trust in third-party data
aggregators.
4.2.Security Attack on Physical Hardware
Attackers can leverage network access to cause actual physical damage, such as draining nodes’
batteries or shutting down meters [15]. Direct meter tampering (e.g., via JTAG interfaces) is also
a concern, as it can reveal sensitive cryptographic keys [53]. Table 2 presents examples of
hardware-targeted attacks.
Table 2. Security issues (physical attacks) with a smart meter.
Author Security issue Implemented Solution Disadvantages and
weaknesses
(Guha et al.,
2019)
Hardware Trojan
attacks on
reconfigurable
hardware.
Ensuring reliability for
periodic & non-periodic
tasks.
Practical, full-scale prevention
strategy lacking.
(Zou et al.,
2020)
Cyber-physical
attacks on the smart
grid.
Parameter correction via
Jacobian matrix & Taylor
approximation.
Limited testing scope on IEEE
14 & 118 bus systems.
(Attia et al.,
2018)
Price manipulation
attack.
Lightweight detection
algorithm
Assumes the control center is
fully trusted.
(Gunduz et
al., 2020)
Malware-based
physical tampering
Cyber-attack model &
detection mechanisms.
Hardware spoofing & physical
tampering are still possible.
(Shao et al.,
2021)
Cooling load
injection (thermal
attack)
Monitoring & detection
algorithms for behind-the-
meter threats.
Need complex equipment for
detection.
4.3. Attacks on Data
Smart meter data contains personal and billing information, making them a valuable target.
Attackers can modify readings to affect billing, pricing calculations (LMP), or glean user
behaviour [58], [59].
Methods like false data injection or ciphertext-only attacks can compromise the integrity and
confidentiality of real-time consumption data [30][36]. Table 3 offers notable examples.

8
Table 3. Security issues (data attacks) with a smart meter.
Author Security issue Implemented Solution Disadvantages and
weaknesses
(Shen et al.,
2020)
Malicious data
mining attack.
Data aggregation scheme for
verifying malicious activity
Slightly higher
communication costs
(Li et al.,
2022)
False data injection
(FDIA)
Secure federated learning with
Paillier cryptosystem.
Lacks discussion on real-
world integration.
(Chen et al.,
2019)
Dynamic states
under data injection
attacks.
Online detection for dynamic
state estimation vulnerabilities.
Damage impact analysis is
limited.
(Eltayieb et
al., 2019)
Cloud-based data
storage & searching
Attribute-based encryption
scheme (online/offline).
Large data volumes require
robust storage strategies.
Core security properties, integrity, availability, confidentiality, and non-repudiation, must be
upheld [34]. Attackers may impersonate legitimate systems (compromising confidentiality or
integrity) or disrupt signals (violating availability) [35]. Encryption, digital signatures, and secure
channel protocols are vital to prevent data theft [36].
Table 4 provides an overview of the main security concerns identified across various studies in
the context of smart meters. Specifically, it summarizes which core security properties, namely
data integrity, data privacy, data confidentiality, data availability, and data authentication, have
been addressed or highlighted as areas of concern by different authors. This comparative analysis
includes contributions from recent literature spanning multiple years, reflecting the evolving
focus and priorities in smart meter security research. The presence or absence of attention to each
security property is indicated for each referenced work, thereby enabling a clear understanding of
the current research landscape and revealing potential gaps that warrant further investigation. This
table serves as a valuable reference for identifying trends and directing future efforts toward more
comprehensive and balanced security frameworks in smart metering systems.
Table 4. Some of the concerns in terms of security properties in smart meters.
Author, year Data
integrity
Data
privacy
Data
Confidentiality
Data
Availability
Data
Authentication
(Khattak et
al.,2019)
No ⌧ Yes☑ No ⌧ Yes☑ No ⌧
(P. Kumar et
al. 2019)
Yes☑ No ⌧ No ⌧ No ⌧ No ⌧
(Abdalzaher,
et al.,2022)
Yes☑ Yes☑ Yes☑ No ⌧ Yes☑
(Garg et al.
2020)
Yes☑ Yes☑ Yes☑ Yes☑ No ⌧
(Orlando et al.
2022)
Yes☑ Yes☑ Yes☑ Yes☑ Yes☑
(Y. Wanget
al.2019)
Yes☑ Yes☑ No ⌧ No ⌧ Yes☑
(Islam, Baig,
2019)
(Mood et al.
2020)
(Kamal ,2019) No ⌧ No ⌧ No ⌧ Yes☑ Yes☑
(Harishma et
al. 2022)
No ⌧ Yes☑ No ⌧ Yes☑ Yes☑

9
Author, year Data
integrity
Data
privacy
Data
Confidentiality
Data
Availability
Data
Authentication
(Avancini et
al. 2021)
No ⌧ Yes☑ Yes☑ Yes☑ Yes☑
(Sun et al.
2021)
Yes☑ Yes☑ Yes☑ Yes☑ Yes☑
(Sureshkumar
et al. 2020)
No ⌧ Yes☑ No ⌧ No ⌧ No ⌧
(Farokhi
2020)
Yes☑ No ⌧ Yes☑ No ⌧ Yes☑
(Zhang, Rong,
2020)
No ⌧ Yes☑ Yes☑ Yes☑ Yes☑
(Shrestha et al.
2020)
No ⌧ Yes☑ No ⌧ No ⌧ Yes☑
(Chakraborty
et al. 2021)
Yes☑ Yes☑ No ⌧ Yes☑ Yes☑
The results reveal that data privacy and authentication/identification were the most commonly
addressed features, each appearing in 87% of the reviewed works. This trend underscores the
increasing emphasis on protecting consumer data and verifying user identity in modern metering
systems.
Data Integrity , 63%
Data Privacy , 87%
Data Confidentiality ,
55%
Data Availability , 62%
Authentication or
Identificathion , 87%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Data Integrity Data Privacy Data Confidentiality Data Availability Authentication or Identificathion
Figure 5. Security properties of smart meter systems.
Figure 5 shows that data privacy and authentication/identification are the most frequently
addressed security concerns in smart metering literature, each covered in 87% of the reviewed
studies. Data integrity 63% and data availability 62% receive moderate attention, while data
confidentiality is less explored, appearing in only 55% of studies. These findings underscore the
need for continued focus on data privacy and authentication as primary security priorities in
future smart metering research and applications.
Table 5 discusses the most famous and most frequent types of attackson smart meter systems and
networks, which have been studied in the literature and past studies: Denial of Service (DoS)
attacks, Man-in-the-middle (MITM)attacks, False data injection attacks (FDIA), Data replay
attacks, Impersonation attacks, and Malicious attacks, as shown in Figure 6.

10
Table 5: Contributions to the Type of Attack on Smart Meters.
Type of attacks Issue Proposed solution
Attacks on the
network
Focus on communication technologies
(e.g., ZigBee with IEEE 802.15.4),
vulnerable to network interruptions,
sniffing, black hole attacks, etc..
Strengthen the network’s routing
mechanism to counter wireless-
based threats.
Attacks on physical
hardware
Cyber-physical exploits can drain nodes’
batteries, crash networks, and cause
physical damage.
Adopt a four-dimensional
approach, time, breadth, depth,
and actor, to safeguard all data
layers and curb physical
tampering.
Attacks on data Stored meter data may be targeted for
manipulation (e.g., altering consumption
records). Attackers range from criminals to
corrupt officials
Implement a security index to
detect manipulated data [61].
Figure 6. The most frequent types of attacks on smart meter systems.
Table 6 provides a comprehensive overview of the security properties and their associated
vulnerabilities within the Smart Meter network. It examines five key security properties, data
integrity, data privacy, data confidentiality, data availability, and authentication, against five
common types of cyberattacks: Denial of Service (DoS), Man-in-the-Middle (MITM),
Impersonation Attack, Replay Attack, and False Data Injection Attack (FDIA). The analysis
indicates that data integrity is particularly vulnerable to MITM, replay, and FDIA attacks, while
data privacy and data confidentiality are primarily compromised by MITM attacks. Data
availability is notably affected by DoS attacks, reflecting its susceptibility to disruptions in
service. Authentication is shown to be at risk from both impersonation and replay attacks. This
classification highlights the specific threats posed by different attack vectors and emphasizes the
need for targeted security mechanisms to protect the critical properties of smart metering systems.

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Table 6. Security properties and vulnerability in the Smart Meter.
5. CONCLUSION
In conclusion, this paper highlights the growing importance of smart meters in modern energy
systems, given their vital role in enabling real-time energy monitoring and communication in
smart grids. While they offer numerous operational benefits to consumers, service providers, and
governments, their use poses significant security and privacy risks that cannot be ignored. Our
main thesis has emphasised that smart meter vulnerabilities fall into three main categories: data,
network, and physical attacks, all of which pose threats to the integrity, availability, and
confidentiality of the system. While some studies indicate the adequacy of current encryption
methods and communication protocols, this review has highlighted their shortcomings in
addressing real-time threats, user privacy concerns, and scalability in resource-constrained
environments. Therefore, we call for continued innovation in lightweight cryptographic protocols,
intrusion detection systems, and privacy-preserving models tailored to the unique constraints of
smart meters.
Looking ahead, future research should prioritize the development of lightweight cryptographic
protocols that maintain strong security guarantees while minimizing computational and energy
overhead. This is especially critical for deployment in resource-constrained environments.
Additionally, there is a pressing need for standardized frameworks that ensure interoperability
across heterogeneous smart grid infrastructures. Real-world implementation also faces challenges
such as key distribution at scale, firmware-level vulnerabilities, secure integration with legacy
grid components, and the threat of insider attacks. Emerging directions include privacy-
preserving machine learning for anomaly detection, blockchain-based decentralized
authentication, and integration of physically unclonable functions (PUFs) for tamper-resistant
hardware. Addressing these challenges will be essential for translating theoretical models into
resilient, real-world smart metering systems.Future research should also focus on practical
applications and standardisation to ensure robust and secure smart metering systems that are in
line with the evolving smart grid landscape.
ACKNOWLEDGEMENTS
This work has been supported by UniversitiTeknikal Malaysia Melaka. The authors gratefully
acknowledge the continuous support from the Center for Research and Innovation Management
(CRIM) UTeM.
Attacks
Properties
(DoS) (MITM) Impersonation
attack
Replay
attack
(FDIA)
Data integrity ⌧ No ☑ Yes ⌧ No ☑ Yes ☑ Yes
Data privacy ⌧ No ⌧ No ⌧ No ⌧ No ⌧ No
Data Confidentiality ⌧ No ☑ Yes ⌧ No ⌧ No ⌧ No
Data Availability ☑ Yes ⌧ No ⌧ No ⌧ No ⌧ No
Authentication ⌧ No ⌧ No ☑ Yes ☑ Yes ⌧ No

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16
AUTHORS
Osama Alshannaq received his Bachelor's degree in Computer Science at
the Faculty of Computer Science in Jordan at Jadara University in 2016. He did
his Master's degree in Computer Science (Internetworking technology) at the
Faculty of Information and Communication Technology, UniversitiTeknikal
Malaysia Melaka (UTeM) in 2019. He is a Lecturer at the Department of
information technology and security, Toledo College, Jordan. He started his
career as a lecturer at this college in May 2021. He has experience in teaching
Computer Science and cryptography. His research interests are in the areas of Computer
Networks, Computer Science, Network Security, Data Security, Data Privacy and Integrity, and
Cryptography. Currently, he is doing his Ph.D. in the network security department at
UniversitiTeknikal Malaysia Melaka (UTeM). He can be contacted at email:
osamaalshannaq99@gmail.com.
Mohd Rizuan Baharon received the PhD degree in Computer Science
from Liverpool John Moores University, Liverpool, United Kingdom, in 2017.
He completed his master degree in Mathematics in 2006 and his undergraduate
studies in 2004 at UniversitiTeknologi Malaysia, Malaysia. Currently, he is a
Senior Lecturer at the Department of Computer System and Communication,
Faculty of Information and Communication Technology, UniversitiTeknikal
Malaysia Melaka, Malaysia. He started his career as a lecturer at this university
since June 2006. He has vast experience in teaching Computer Science, Cryptography and
Mathematics subjects. His research interests are mainly in the area of Mobile Network Security,
Cloud Computing Security, Data Privacy and Integrity, Mobile Users Accountability and
Cryptography. He is a lifetime member of Mathematical and Sciences Association Malaysia
(PERSAMA, Malaysia). He has produced a number of journal and conference papers at national
and international levels. He can be contacted at email: mohd.rizuan@utem.edu.my
Shekh Faisal Abdul Latip is currently working at the Faculty of Information and
Communication Technology, UniversitiTeknikal Malaysia Melaka (UTeM). He
received his PhD in 2012 from the University of Wollongong, Australia, in the field of
Cryptography. Prior to his PhD studies, he obtained an M.Sc in Information Security
from Royal Holloway, University of London, in 2003. He earned both a B.Sc (Hons) in
Computer Science (2000) and a Diploma in Electronic Engineering (1994) from
UniversitiTeknologi Malaysia (UTM). His main research interest focuses on
symmetric-key cryptography, specifically the design and cryptanalysis of block
ciphers, stream ciphers, hash functions, and MACs. He is currently a member of a focus group and one of
the evaluation panel experts for the MySEAL project, which aims to recommend a list of trusted
cryptographic algorithms for use by public and private sectors in Malaysia. To promote new ideas and
activities in cryptology-related areas in Malaysia, he joined and became a member of the executive
committee of the Malaysian Society for Cryptology Research (MSCR).
Hairol Nizam Mohd Shah received the Diploma (Electric-Electronic) from
UniversitiTeknologi Malaysia in 2000, B.Eng. (Electric-Electronic) from Universiti
Malaysia Sabah in 2004. He received the M. Eng. (Mechatronics) and PhD
(Mechatronics) at UniversitiTeknikal Malaysia Melaka in 2008 and 2018. Currently
he is a senior lecturer at the UniversitiTeknikal Malaysia Melaka, Ayer Keroh
Melaka. His primary interests related to vision systems, robotics and computer-
integrated and image processing.

17
Dr Áine MacDermott is a Senior Lecturer in the School of Computer Science and
Mathematics at Liverpool John Moores University (LJMU) in the UK. She obtained
her PhD in Network Security from LJMU in 2017, and a BSc (Hons) in Computer
Forensics in 2011. Áine is a member of Research Centre for Critical Infrastructure
Computer Technology and Protection (PROTECT) at LJMU, with research interests
including the Internet of Things, collaborative intrusion detection in interconnected
networks, digital forensics, and machine learning.

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