![2 Cyber-Physical Systems
of previously learned data. Hence, to provide security to the resource-constrained
IoT environment, selecting the potential input data for the incremental learning
model and fine-tuning the parameters of the deep learning model for the input
data is vital, which assists towards the proactive prediction of the security threats
by the time-efficient learning of the dynamically arriving input data.
Keywords: Industrial IoT, smart manufacturing, industry 4.0, interoperability,
deep learning, incremental learning
1.1 Introduction
In recent years, Industrial Internet of Things (IIoT) technology [1] has
gained significant attention among the internet users in the real-world
with the increased advantage of the ubiquitous connectivity and interac-
tion between the physical and cyber worlds. With the enormously inter-
connected IoT devices, IIoT devices have been used in various applications
such as smart homes, smart cars, smart healthcare, smart agriculture, and
smart retail. The exponential rise of IoT technology often confronts secu-
rity and privacy concerns [2]. Nowadays, cyber-attacks such as ransom-
ware and malware have increasingly targeted IoT applications to impact
the distributed network. Even though the existing security measures are
adopted in the IoT environment, IIoT applications are still vulnerable to
different attacks due to the massive attack surface [3, 4]. Hence, it is essen-
tial to design the defense mechanisms to detect and predict the attacks
in the IIoT platform. Applying the traditional security models or mecha-
nisms is inadequate for the IIoT environment due to the intrinsic resource
and computational constraints. Intrusion detection models dynamically
monitor abnormal behaviors or patterns in the system to detect malicious
activity. The existing intrusion detection researches have mainly focused
on rule-based detection techniques, which lack to support the detection
of anomalies in the emerging IIoT platform [5]. To detect anomalies with-
out false alarms, artificial intelligence methods have been widely used by
security researchers. For the most part, in order to deal with the massive
amount of data generated by IoT devices, machine learning and deep
learning algorithms have been used to perform automated data analysis as
well as to provide meaningful interpretations [6, 7]. Several research works
have employed machine learning and deep learning techniques to detect
malicious activity in the IIoT environment. Despite the combination of
intrusion detection and artificial intelligence-based research, it still con-
fronts the precise detection of anomalies in IIoT networks.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-28-320.jpg)
![Cyber Security Threats of Industrial Internet of Things 3
Owing to the dynamic arrival of the new malware classes and
instances in the IIoT platform, traditional machine learning, and deep
learning-based security models deal with the catastrophic forgetting prob-
lems. Catastrophic forgetting is the ignorance of the knowledge about pre-
vious significant classes while performing the classification for the new
classes. The security experts have widely utilized incremental learning
models [8, 9]. The incremental learning model continuously learns the
new data with the knowledge of the previous learning results. It plays a
significant role in improving the detection or prediction performance in
developing the security models for the detection of known and unknown
attacks. The incremental learning model often confronts the stability-
plasticity problem: previous data retaining and new data preserving [10].
Hence, harvesting useful insights from the enormous amount of data are
crucial to improve the learning performance. In essence, preprocessing the
continuously arriving data streams to augment the training data is crucial
for the incremental learning model. Thus, this work focuses on modeling
the security mechanism for the IIoT application with the contextual pre-
processing and the enhanced deep incremental learning model. With the
target of improving the detection performance, it employs the incremental
feature selection with optimization for the contextual preprocessing and
fine-tunes the learning parameters for the proactive prediction of the mali-
cious activities in the IIoT environment.
1.2 Background of Industrial Internet of Things
The Fourth Industrial Revolution (4.0) paradigm can be thought of as
a road map that takes us through the four industrial revolutions in the
development of manual-to-market industrial production processes. Figure
1.1 illustrates the process of creation. With the beginning of the First
Industrial Revolution in the 1800s came the development of mechaniza-
tion and electric power generation [11]. When mechanical and mechanical
power were introduced in the 1800s, the very first Industrial Revolution
was launched (Figure 1.2). This resulted in the transition away from phys-
ical labor toward the very first methods of production, which was partic-
ularly noticeable in the textile industry [12]. The improved overall quality
of life played a significant role in the transition process, according to the
researchers. Because of the electrification of the world, millions of peo-
ple were able to industrialize and develop, sparking the Second Industrial
Revolution [13]. To illustrate this point, consider the following quote from
Henry Ford, which refers to the Ford T-Model automobile: “You can have](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-29-320.jpg)
![4 Cyber-Physical Systems
any colour as long as it is black.” Although mass production is becoming
increasingly popular, there is still room for product customization if mass
production is not used. It is the third industrial revolution, which began
with the introduction of microelectronics and automation and has con-
tinued to the present day [14]. Module manufacturing is encouraged as a
result of this, in which a variety of items is created on flexible production
lines by employing programmable machines as well as various materials
[15].
These manufacturing processes, on the other hand, are limited in their
ability to accommodate varying output volumes, which is a disadvantage.
The fourth industrial revolution has begun as a result of the advancement
of information and communications technology (ICT). Intelligent auto-
mation of cyber-physical systems with decentralized control and advanced
networking is the technological foundation for artificial intelligence-based
systems. Intelligent automation of cyber-physical systems with decentral-
ized control and advanced networking is based on decentralized control
Industry 1.0
Industry 2.0 Industry 3.0 Industry 4.0
IoT, Cyber
Security
1969
Automation,
Computer,
Electronics
1870
Mass
Production,
Electrical
energy
1784
Mechanization,
Steam power,
Weaving loom
Figure 1.2 The industrial revolutions.
Lightweight
Security
Solution
Parameter
Updating for
new data
Noise – less
Augmented
training data
Figure 1.1 Challenges in artificial intelligence-based IIoT security model.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-30-320.jpg)
![Cyber Security Threats of Industrial Internet of Things 5
and advanced networking (IoT functionalities) [25, 26]. A self-organizing
cyber-physical production structure was created by reorienting this new
industrial production technology using classical hierarchical automa-
tion systems. As a result of this new manufacturing technology, scalable
mass-customized production as well as flexibility in terms of production
volume are now possible.
Research Gap
The existing security researchers have handled the different types of attacks
on the IIoT network by adopting the deep learning and incremental learn-
ing models; however, the incremental learning-based security models have
been confronted with several shortcomings particularly, in the IIoT net-
work, which are discussed as follows.
• Applying the available existing IIoT security solutions is crit-
ical due to the primary concern of the resource constraints
in the IIoT network.
• Owing to the need for cross-layer design and optimization
algorithms for the security mechanisms, the available secu-
rity solutions are inappropriate for the IIoT model.
• The DDoS or intrusion detection models often confront the
increased probability of false positives, leading to ineffective
attack detection [16].
• Lack of modification in the machine learning model while
adopting the security solution leads to an increased number
of false positives and true negatives.
• Traditional deep learning models lack the development of a
reliable, robust, and intelligent security mechanism over the
massive scale deployment of the IIoT.
• Static machine learning and deep learning models lead to
inaccurate decision-making due to the continuously arriv-
ing data streams from different IIoT data sources [17].
• Incrementally identifying the potential features and making
the decisions from the extracted set of features over the con-
tinuously arriving data streams is critical.
• Traditional preprocessing methods lack to support the effec-
tive incremental learning results due to the variations in the
inherent relationships of the arriving data [18].
• Incremental learning models lead to inaccurate
decision-making without handling the drift data in the](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-31-320.jpg)
![6 Cyber-Physical Systems
IIoT applications due to the enormous availability of the
continuously changing data.
• Modeling the deep learning algorithm with the appropriate
parameter values is quite critical for detecting known and
unknown attacks in the dynamic IIoT environment.
Challenges in IIoT Security
In the real-world, the IIoT applications often demand both the speed and
accuracy ensured data stream mining methods. The IIoT platform con-
fronts major security issues due to the ever-increasing complexity of the
attacks, zero-day vulnerabilities, the nature of connected IIoT devices, and
the lack of detection of new threats. The existing IIoT security models lack
in providing suitable security solutions over the continuous arrival of the
IIoT data. Owing to the resource-constrained IIoT environment, model-
ing the heavy-weight security solution is inappropriate. Even though tradi-
tional machine learning and deep learning techniques have been adopted
to model the IIoT security solutions, effectively detecting over the contin-
uously arriving IIoT data and developing the lightweight security solution
is challenging [19]. The continuous arrival of IIoT data leads to the inac-
curate detection or classification of the malicious activities due to the exis-
tence of the noisy data, which also leads to the increased computational
time. Besides, detecting the new malware or attacks in the IIoT environ-
ment with a large number of training samples by the traditional learning
model is ineffective [20]. To overcome this obstacle, the incremental learn-
ing models have been utilized by the IIoT security researchers. However,
training the massive amount of arriving data streams and detecting both
the known and unknown malware without selecting the potential fea-
tures is critical. Hence, there is an essential need to preprocess the massive
data streams and protect the IIoT environment from both the known and
unknown malware-based attacks [21].
1.3 Literature Review
Several progressive and online algorithms have been written, mostly
adapting the existing batch techniques to the progressive environment.
Massive theoretical work was done in the stationary environment to test
their capacity for generalization and convergence speed, often followed by
assumptions such as the linear details. While progress and online learning
are well developed and well founded, some publications are only generally](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-32-320.jpg)
![Cyber Security Threats of Industrial Internet of Things 7
aimed at the elder, especially in the context of big data or the Internet of
Things technology. Most of these are surveys that classify available meth-
ods and certain fields of application.
The principle of progressive learning with a certain motivation for incre-
mental learning is included in Giraud-Carrier and Christophe [15]. They
promote progressive learning approaches to incremental projects and also
illustrate problems such as e-effects ordering or a trustworthiness query.
Gepperth and Hammer recently conducted a survey. Usually, the num-
ber of measurements and the number of incoming data instances can be
approximated. It can also be presumed how critical the rapid response of
the system is. It can also be guessed if a linear classifier is suitable for such
tasks.
Challenges in the Environment
An overview of commonly used algorithms with relevant implementation
of the real world is also given see Table 1.1.
Incremental learning is done more broadly in streaming environments,
but much of the work is geared towards drifting ideas.
Main Properties for Incremental Algorithms for Domingos and Hulten
To sustain the increasingly growing data rate, production, they emphasize
the importance of combining models with theoretical performance guar-
antees, which are strictly limited in time and space processing.
Batch-incremental methods were contrasted and evaluated with
examples-incremental methods. The inference is, for example, that incre-
mental algorithms are equally effective, but use less energy and that the
lazy strategies function especially well with a slider.
Fernandez et al. conducted a big test of 179 batch classes on 121 datasets.
This comprehensive analysis also included several implementations trendy
various toolboxes and languages. The best results were achieved with the
Random Forest algorithm [24] and the Gaussian supporting kernel vector
Machine (SVM) [25]. However, for incremental algorithms such work is
still desperately missing. In this chapter, we take a qualitative approach
and examine in depth the main approaches in stationary settings, instead
of a broad comparison. We also track the complexity of the model, which
takes time and space to draw the required resources, in addition to accu-
racy. Our analysis ends with some unknown considerations, such as con-
vergence speed and HPO.
In machine learning, deep learning is a subfield that is concerned with
learning a hierarchy of data inputs. Many areas such as image detection,
speech recognition, signal processing, and natural language processing](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-33-320.jpg)
![12 Cyber-Physical Systems
have now been enriched by deep learning algorithms, which have been
learned by researchers in order to solve problems.
Deep learning methods are a category of learning methods that can
hierarchically learn characteristics from the lower to higher level by
constructing a deep architecture. The deep learning methods are able to
learn features on several levels automatically, which enable the algorithm
to learn complex mapping functions directly from data without human
characteristics.
The key characteristic of profound methods of learning is that their
models are all profoundly architectured. A deep architecture means that
the network has many secret layers. A shallow architecture, in comparison,
has only few hidden layers (one to two layers).
Deep neural networks are effectively implemented in different fields:
regression, classification, size reduction, movement modeling, texture
modeling, information retrieval, processing of natural languages, robotics,
error diagnosis and road cracks.
In the ML model, a set of 21 feed profound neural networks was created,
which included a variety of DNN values, such as the number of hidden lay-
ers, the number of processing units per layer, the triggering of functions,
and methods of optimization and regulation. The permutation method
[22] has been used to determine the relative value in the ensemble’s accu-
racy of the various biochemical markers. Standardization batch [23] was
used to minimize overfit effects and improve the stability of the model’s
convergence.The best results were obtained by using a DNN with five hid-
den layers and the regularised mean squared error (MES) function for loss
estimation in the loss estimation, the activation PReLU function (PReLU)
[24] for each layer and the loss optimization AdaGrad [25] for each layer.
The highest DNN score with 82% accuracy was β = 10, i.e. when the pre-
dicted age was ±10 years of true age, it found the sample to be correctly
accepted, exceeding many groups of the competing ML models. Several
models were evaluated for the combination of each DNN into an ensemble
(stacking), and the elastic net model was most successful [26]. Albumin,
glucose, alkaline phosphatase, urea and erythrocyte have been the most
effective blood markers.
This model should be incremental learning as well deep learning in
industrial IoT.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-38-320.jpg)
![20 Cyber-Physical Systems
A cyber-physical system is the association of cyber and physical com-
ponents that have been specifically engineered to monitor, coordinate
and control based on computational algorithms. It is a 3C technology—
communication, computation, and control. Cyber-physical systems
capture the data from the wireless sensor devices and monitor them, the
control of the physical devices is based on the physical data using actua-
tors, thus interacting both with the physical and cyber world in the real
environment. These systems are interconnected with each other on a uni-
versal scale using different network and communication resources. The
physical control is efficient when the data collected from the sensors are
processed for information with data mining techniques. The interaction
among the users from context perspective, the physical device’s surround-
ings and the process in the cyber-physical systems are observed when the
features of cyber-physical system are considered. However, the integration
rules, interoperation among the devices, control of cyber-physical system
are the functions that are globally distributed and networked in real-time
[1]. This system is used extensively in many applications such as industries,
transport and vehicular industry, medical and health management, smart
grids, military applications, weather forecasting and many more.
An enormous measure of data is generated from various digital tech-
nologies like wireless detectors and sensors, mobile phones, storage
devices connected to the internet where a continuous data stream is pro-
duced. Cyber-physical system has a computational capability that needs
to be scaled to provide efficiency as the increasing sensors, digital tech-
nologies and devices that are networked create a huge volume of data. To
develop a system that is more efficient, intelligent, reliable, trustworthy and
self-adaptable integration of big data into a cyber-physical system is manda-
tory. Computing and computational resources are comparatively lower than
the huge data generated from various resources. The big data analytics tech-
niques aim to examine, process and handle the big data characteristics of
data to identify the patterns, obtain the information that is needed and rela-
tionships in the data sets also the innovative forms of data can be obtained
for decision making and process control. The insights about how to model,
capture, specify, transfer, organize and manage the data efficiently can be
discovered [2]. Conventional data analytics processes the data sets the
whole size or type, whereas big data analytics collect, process the data and
manage them with low latency and typical data such as unorganized data,
data gathered from the sensors including the ones that have spatiotemporal
characteristics and the data produced in real-time considered as the stream
of data flow can be composed with faster results during real-time process-
ing. Machine learning (ML), artificial neural networks (ANN), statistics,](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-46-320.jpg)
![Big Data Analytics in Cyber-Physical Systems 21
dynamic Bayesian networks (DBA), deep learning, and natural language
processing are some of the advanced big data techniques. The merging the
big data analytics with the cyber-physical system is inevitable as it is the key
to productive, efficient and adaptable cyber-physical system to sustain.
The following are the contents discussed in the chapter. Section 2.2 con-
tains the architecture of cyber-physical system from a big data model for
cyber-physical system. Section 2.3 explains the issues and challenges when
big data is integrated with cyber-physical system, integration of CPS and
BDA and its control and management. The storage and its communication
of big data for cyber-physical system are stated in Section 2.4. Data pro-
cessing techniques and models of big data such as cloud and multi-cloud
processing, clustering in big data and cyber-physical system and big data
analytics models are stated in Section 2.5. Applications of big data-enabled
CPS are stated in Section 2.6 particularly manufacturing, smart grids and
smart cities, healthcare and smart transportation. The data security and
privacy from the CPS applications and loop holes that cause cyber threats
in big data analytics are further discussed.
2.2 Big Data Model for Cyber-Physical System
Thebigdatacharacteristicscanbeunderstoodby5Varchitecture—volume,
variety, veracity, velocity and value [3]. Big data analytics (BDA) is applied
in many distinct domains such as e-commerce, enterprise to predict the
patterns of customers’ interest, and weather forecasting, where changes
in the weather can be analyzed and pattern prediction is done based on
past data, etc. The data characteristics are varied and the implementation
of aggregated data cost is considered due to which smart data was pro-
posed. The concept of smart data is to make sure to eliminate the noise
so that important and relevant data can be obtained, which can further
be used for application purpose in cyber-physical system to monitor and
control so that accurate decision can be made which impacts the physical
device in the real-time environment [4]. The present BDA models that are
used focus on mining the data, functions that process the data along with
data storage and visualization instead of exploring the ways that big data
acquire smart data from raw data which makes the integration vulnerable
and lowering the analytic capabilities of the system. The BDA architecture
should improve the effectiveness and intelligence of the cyber-physical sys-
tem. The communication layer is included in the system architecture for
smart data purpose, data source layer is included in the BDA model which
integrates smart methods for data mining and visualizing layer that aids in](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-47-320.jpg)
![22 Cyber-Physical Systems
the integration of collection, pre-processing, storage, mining and visual-
ization of data functions in CPS [5].
2.2.1 Cyber-Physical System Architecture
The BDA enabled CPS design comprises of three layers namely—a physical
layer, a cyber layer and a communication layer.
Physical layer—Sensors that are locally distributed across the CPS appli-
cation fields generate data that are accumulated in the layer for further
process. This data contains noise and are uncertain which can be termed as
raw data and needs to be processed.
Communication layer—This layer pre-processes the raw data into smart data
and converts the decisions from the cyber layer to executable commands.
Cyber layer—Controlling and monitoring decisions are made by analysing
the data that reflects in the infrastructure of the physical layer.
State sensing, intelligent analysis in real-time, accurate execution and
self-optimization are some of the main functions of the architecture from
a data processing perspective.
2.2.2 Big Data Analytics Model
The BDA is the other section of the architecture—a vast amount of raw
data is processed so that decisions are made faster and better. The learn-
ing process in the BDA model is inspired by the human brain, techniques
(support vector machine, fuzzy clustering, convolutional neural networks,
auto-encoders, deep learning models) that are integrated with data pro-
cessing techniques [6]. The big data analytics model contains four layers—
the data source layer, smart data warehouse layer, smart data mining layer
and smart visualization layer.
Data source layer—Many technologies are used to gather data in this layer.
Raw data is collected from distributed wireless sensors that include industrial
applications, social media, the internet, etc. from the physical CPS devices.
Smart data warehouse layer—This layer manages and maintains histori-
cal data that aids decision making and provides an environment to anal-
yse information [7]. The raw data is processed into information with the
aid of a data cleaning module that removes the inaccurate record, a data](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-48-320.jpg)
![24 Cyber-Physical Systems
data to make decisions. Apart from this, spatial data is also taken into account
as it plays important part in the big data-enabled cyber-physical systems.
2.3.1.1 Integration of CPS With BDA
To enable the integration of two systems, an Architecture Analysis and
Design Language (AADL) [8], Modelica modeling language—Modelicaml
[9] and clock theory [10] integration ensures that the requirements of big
data are met and are implemented on the platforms of big data and its prop-
erties are considered [11]. A vector-logical big data processing approach,
that lets cyber-physical systems control the operations and a computing
automation model that impacts performance and hardware intricacy is
proposed in the aid of the integration [12].
2.3.1.2 Control and Management of Cyber-Physical System With
Big Data Analytics
The so-called system controls the interconnected devices and systems
between the physical environment and the computational capabilities in
a real-time dynamic environment and manages them. Self-awareness,
self-configuration and self-repairing are some of the abilities that
cyber-physical system has to adapt for the system to sustain.
The big data environment handles the data as a service to deal with,
where this service will be able to manage big data characteristics such
as volume, velocity and variety while gathering the generated data from
the sensors and the machine controls, and organize them based on the
multi-dimensional feature spaces and apply in the industry 4.0 to function
[13]. Some of the challenges here faced are big data acquisition and storage,
widespread data relevance, data stream elaboration, analysing the data and
machine similarity identification, the human–machine interfaces (HMI)
based on certain applications and feedback-control mechanisms.
Managing and control of cyber-physical system always depend on the
modes created by the humans, but hard to verify and maintain as they
are incomplete which leads us to data-driven approaches where the huge
amount of data collected by the CPS are modeled such that they learn auto-
matically the models. Cognitive reference architecture is best preferred in
this context [14]. This analysis of cyber-physical systems includes different
interfaces that interconnect with each other. The big data platform is an
interface that all the relevant raw data from the machines and sensors are
gathered and prepared for analysis and interpretation. The next interface is](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-50-320.jpg)
![Big Data Analytics in Cyber-Physical Systems 25
learning algorithms that brief about the anomaly detection used for mon-
itoring conditions and predictive maintenance from the data. The infor-
mation provided from the learning algorithm interface is combined with
specific domain knowledge to identify faults and semantic context is added
to the results in this conceptual layer. The results from the conceptual layer
are converted in a human-understandable manner and implemented to
achieve better standardization, efficiency and repeatability in task-specific
HMI. Another conceptual layer is placed where the use of knowledge is
done to recognize actions that are needed to be taken under the users’ deci-
sions which are needed to be communicated to the next interface. The final
interface is the adaption layer where the computation of commands takes
place in real-time, which communicates changes to the control system that
reflects in the physical device.
Modeling the cyber-physical with big data should consider the chaotic
features caused by the control of cyber-physical system as it deals with
the vast amount of data and its control so that it may lead to unpredicted
results. The cyber-physical system responds to all the minor changes and
disturbances which cause the system to be sensitive. A fuzzy feedback lin-
earization model followed by a time prediction algorithm is initiated to
tackle the chaotic control problems in CPS and also including the synchro-
nization control problem [15].
2.3.2 Issues and Challenges for Big Data-Enabled
Cyber-Physical System
The big data-driven CPS will consider the special characteristics and attri-
butes, restrictions, demands and constraints along with the basic big data
properties—volume, velocity, variety, volatility, value, veracity and validity
that are met during the development of certain system domain integrated
with big data. The functional components of big data in CPS are system
infrastructure and data analytics which should be considered during the
integration. Real-time communication between the physical and cyber
devices, where capturing the data, monitoring the database and its func-
tionalities and the distributed computing is part of the system infrastruc-
ture component. Data analytics deals with product actualization and
resource efficiency and organization along with predictive and descriptive
analysis. Some other important issues that deal with both the components
are adaptability, flexibility, security and reliability.
In cyber-physical system, a vast amount of data from networking sen-
sors, machines, and several other embedded devices are collected from](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-51-320.jpg)
![26 Cyber-Physical Systems
the physical environment. These data-producing devices such as sensors
are not restricted to a certain time or space and also several category and
forms such as temperature, speed, geographical data, environmental data,
astronomical data, health and logistics data from different sectors and also
from digital equipment, transportation and public facilities and smart
homes. This leads us to spatiotemporal data requirements, where the sys-
tem mostly functions in a real-time environment which makes us consider
the spatial and temporal data. Geographical data, time-series data, data
from remote locations and from moving object trajectories—where data
contains movement history of objects are considered as spatiotemporal
data.
The time and space correlations are to be considered as important
cyber-physical system data features, where the dimensions of such data
are observed during analysis and processing. The heterogeneous data are
most common in cyber-physical system and the data representation and
model makes the data more insightful. Real-time support, sensing and
communication services availability, maintenance, infrastructure for the
system, evolvability, modularity challenges are persistent when the inte-
gration takes place. This integration also questions the infrastructure of the
cyber-physical system where the communication and computational capa-
bilities needed to be inspected. Security is another important challenge
as its standards vary from applications when they interact with different
devices. The control decisions, the trustworthiness of data and authentica-
tion of devices and their management where there is a necessity to inter-
pret the protocols and approach towards the system in specific applications
as security demands [16].
2.4 Storage and Communication of Big Data
for Cyber-Physical System
The management of data and communication in the real environment is
key for a successful system to function and sustain constantly with effi-
ciency. Managing the storage operation for cyber-physical system with big
data solutions should be regarded alongside caching and routing as there is
a huge amount of traffic from the social media applications, people health
data, traffic and weather monitoring applications and other smart home
appliances which led to the researchers find solutions in storage and com-
munications of big data CPS. Enhancing the performance of system needs](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-52-320.jpg)
![Big Data Analytics in Cyber-Physical Systems 27
to concentrate on the improvement of data collection, data processing
techniques from a storage perspective.
2.4.1 Big Data Storage for Cyber-Physical System
Storing the persistent and continual data from numerous resources
demands that the approaches be efficient and effective from a scalability,
cost and flexibility perspective. Combining the cloud/edge computing
facilities with big data analytics can give significant results for data storage
objectives. Innovative measures should be applied such as proactive con-
tent caching in the networks and its characteristics that predict the user
behaviour is the motivation for big data-enabled architectures where data
and statistical analysis and visualizations methods are taken into account
at base stations. To satisfy the users the data is controlled and used for con-
tent popularity estimation and content caching in which cyber-physical
system has a high interest [17].
Pre-cache technologies are used with big data for higher performances
during the transfer of data from sensors to servers, given that cyber-
physical system generates a vast amount of data, where network traffics are
caused. Two differential algorithms namely Data Filter Algorithm (DFA)
and Data Assembler on Server Algorithm (DASA) are used to reduce the
traffic in the networks during the data transfer [18]. This can be implied
as an optimal trade-off solution that resolves the network traffic problem
effectively and also the data accuracy problem where the data captured by
the sensors are changed slightly due to the accuracy of the sensors. The
data accuracy is dealt with by choosing the relevant parameters and the
algorithm functions before sending the data to the servers by using filters
and places them in the sensors and a measure is assigned to each.
Performing the caching on the wireless sensor networks, device-to-
device networks in wireless environment and its caching and other data
generation devices like base stations rather than on the clouds offers a pos-
itive impact on data management. Coded multi transmission is used at
the base stations for caching in a realistic environment which allows sharp
attributes and quality of the throughput in the asymptotic regime of the
sensors which is based on a simple protocol model that uses geometric link
conflict constraints and captures elementary aspects of the interference and
spatial spectrum reuse [19]. The integration of big data with real-time CPS
finds these caching and storage techniques very useful where reliability
and predictability are preferred first and different strategies to enhance the
CPS performance can be used to speed up the data collection, processing](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-53-320.jpg)
![28 Cyber-Physical Systems
and distribution and the correct use of caching techniques makes the sys-
tem more manageable.
2.4.2 Big Data Communication for Cyber-Physical System
Cyber-physical system makes decisions considering the data generated
from the sensors newly created by the digital technologies which provide
information and is used for processing. The innovations in big data tech-
nologies provide new insights into the effect of strategic communication,
the communication process needs to be analyzed and controlled along
with the management of information in real-time evaluation. Modern
ways of thinking and decision making are one of the prominent promises
that big data computing offers. The data is always made available to the
users’ advantage so that optimal decisions can be made by determining
the latest information which gives more accurate results. Big data delivery
technology can be a key technology that does computing better. The big
data transmission requirements are to be considered and met among the
big data characteristics, which is challenging to process the data where the
limited transmission capabilities are to be observed.
The big data environment should be made familiar for cyber-physical
systems by proposing new architectures, network infrastructure and other
services that have become vital. The data delivery performance should be
improved for betterment in the device-to-device (D2D) communications.
Without support from the network infrastructure or central control units,
the data is exchanged among the nodes. There are certain limitations in
the data delivery capacity in D2D communications when the quality and
mobility of the nodes are considered. As the cognitive radio technology
is integrated with D2D communications, the cognitive radio technology
gives the device-to-device the ability to improve the data delivery capacity
and makes D2D an alternative that acts as supporting system for the appli-
cations of big data [20]. The routing algorithms for D2D cognitive radio
networks should be appropriately chosen along with its communication.
Integrating the wireless sensor network with mobile cloud computing cre-
ates significant advantages where WSN have distributed sensors spatially
that monitor the physical conditions such as temperature, sound, pres-
sure, motion, light etc. that changed the way that interaction takes place
with the physical world, whereas mobile cloud computing appears to be
the new computing model with efficiency, powerful and unique comput-
ing basics such as processors, storage, applications and services offered in
networks which can be accessed easily on demand. Lower operating cost,
high scalability, easy accessibility and maintenance expense are some of the](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-54-320.jpg)
![Her middle ye weel mot[146] span;
He’s thrown to her his gay mantle,
Says, ‘Lady, hap[147] your lingcan[148].’
VI
Her teeth were a’ like teather stakes[149],
Her nose like club or mell[150];
An’ I ken naething she ’pear’d to be
But the fiend that wons[151] in hell.
VII
‘Some meat, some meat, ye King Henry,
Some meat ye gie to me!’—
‘An’ what meat’s in this house, ladye,
That ye’re not welcome tae?’—
‘O ye’se gae[152] kill your berry-brown steed,
And serve him up to me.’
VIII
O whan he slew his berry-brown steed,
Wow but his heart was sair!
She ate him a’ up, skin an’ bane,
Left naething but hide an’ hair.
IX
‘Mair meat, mair meat, ye King Henry,
Mair meat ye gie to me!’—
‘An’ what meat’s in this house, ladye,
That ye’re not welcome tae?’—
‘O do ye slay your good grey-hounds](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-56-320.jpg)
![XIV
O he’s sew’d up the bluidy hide,
A puncheon o’ wine put in;
She’s drunk it a’ up at a waught[153],
Left na ae drap ahin’[154].
XV
‘A bed, a bed, now King Henry,
A bed ye’se mak’ to me!’—
‘An’ what’s the bed in this house, ladye,
That ye’re not welcome tae?’—
‘O ye maun pu’ the heather green,
An’ mak’ a bed to me.’
XVI
Syne pu’d he has the heather green,
An’ made to her a bed,
An’ up has he ta’en his gay mantle,
An’ o’er it he has spread.
XVII
‘Tak’ off your claiths now, King Henry,
An’ lie down by my side!’—
‘O God forbid,’ says King Henry,
‘That ever the like betide;
That ever a fiend that wons in hell
Shou’d streak[155] down by my side!’
XVIII](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-58-320.jpg)
![But whan day was come, and night was gane,
An’ the sun shone thro’ the ha’,
The fairest ladye that ever was seen
[Cam’ to his armès twa].
XIX
‘O weel is me!’ says King Henry,
‘How lang’ll this last wi’ me?’
Then out an’ spake that fair ladye,
‘Even till the day you dee.
XX
‘For I’ve met wi’ many a gentle knight
That’s gien me sic a fill;
But never before wi’ a courteous knight
That ga’e me a’ my will.’
FOOTNOTES:
[140] routh = plenty.
[141] burd-alone = lone as a maid.
[142] jelly = jolly, jovial.
[143] bierly = stout, handsome.
[144] fleer = floor.
[145] hat = hit.
[146] mot = might.
[147] hap = cover.
[148] lingcan for lycam = body.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-59-320.jpg)
![[149] teather stakes = tether pegs.
[150] mell = mallet.
[151] wons = dwells.
[152] ye’se gae = you shall go.
[153] waught = draught.
[154] ahin’ = behind.
[155] streak = stretch.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-60-320.jpg)
![I
In the third day of May
To Carleile did come
A kind curteous child
That co’ld[156] much of wisdome.
II
A kirtle and a mantle
This child had uppon,
With brauches and ringes
Full richelye bedone[157].
III
He had a sute of silke
About his middle drawne;
Without he co’ld of curtesye
He thought it much shame.
IV
‘God speed thee, King Arthur,
Sitting at thy meate;
And the goodly Queene Guenever!
I cannot her forget.
V
‘I tell you, lords in this hall,
I hett[158] you all heed,
b h](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-62-320.jpg)
![Except you be the more surer
Is for you to dread.’
VI
He pluck’d out of his potener[159],
And longer wo’ld not dwell,
He pull’d forth a pretty mantle
Betweene two nut-shells.
VII
‘Have thou here, King Arthur,
Have thou here of mee:
Give itt to thy comely queene
Shapen as itt is alreadye.
VIII
‘Itt shall never become that wiffe
That hath once done amisse.’
Then every knight in the king’s court
Began to care[160] for his.
IX
Forth came dame Guenever,
To the mantle she her bed[161];
The ladye shee was new fangle[162]
But yett she was affrayd.
X
h h h d k h l](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-63-320.jpg)
![When shee had taken the mantle,
She stoode as shee had beene madd;
It was from the top to the toe
As sheeres had it shread.
XI
One while was it gaule[163],
Another while was itt greene,
Another while was it wadded[164];
Ill itt did her beseeme.
XII
Another while it was blacke,
And bore the worst hue:
‘By my troth,’ quoth King Arthur,
‘I thinke thou be not true.’
XIII
Shee threw downe the mantle,
That bright was of blee[165];
Fast with a rudd red
To her chamber can[166] she flee.
XIV
She cursed the weaver and the walker[167]
That cloth that had wrought,
And bade a vengeance on his crowne
That hither hath itt brought.
XV](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-64-320.jpg)
![XX
She threw down the mantle
That bright was of blee,
Fast with a red rudd[168]
To her chamber can she flee.
XXI
Forth came an old Knight
Pattering ore a creede,
And he proferr’d to this little Boy
Twenty markes to his meede;
XXII
And all the time of Christmasse
Willingly to ffeede;
For why[169] this mantle might
Doe his wiffe some need.
XXIII
When shee had tane the mantle
Of cloth that was made,
Shee had no more left on her
But a tassell and a threed:
That every knight in the King’s court
Bade evill might shee speed.
XXIV
She threw downe the mantle](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-66-320.jpg)
![She threw downe the mantle,
That bright was of blee,
Fast with a red rudd
To her chamber can she flee.
XXV
Craddocke call’d forth his ladye
And bade her come in;
Saith, ‘Winne this mantle, ladye,
With a little dinne[170].
XXVI
‘Winne this mantle, ladye,
And it shal be thine
If thou never did amisse
Since thou wast mine.’
XXVII
Forth came Craddocke’s ladye
Shortlye and anon,
But boldlye to the mantle
Then is shee gone.
XXVIII
When she had tane the mantle
And cast it her about,
Up at her great toe
It began to crinkle and crowt[171]:
Shee said, ‘Bowe downe, mantle,
And shame me not for nought.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-67-320.jpg)
![XXIX
‘Once I did amisse,
I tell you certainlye,
When Craddocke’s mouth I kist
Under a greenè tree;
When I kist Craddocke’s mouth
Before he marryed mee.’
XXX
When shee had her shreeven[172]
And her sinnes shee had tolde,
The mantle stood about her
Right as she wo’ld;
XXXI
Seemelye of coulour,
Glittering like gold
Then every knight in Arthur’s court
Did her behold.
XXXII
The little Boy stoode
Looking over a dore;
[There as he look’d
He was ware of a wyld bore.]
XXXIII](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-68-320.jpg)
![XXXIII
He was ware of a wyld bore
Wo’ld have werryed[173] a man:
He pull’d forth a wood-kniffe
Fast thither that he ran:
He brought in the bore’s head
And quitted him like a man.
XXXIV
He brought in the bore’s head,
And was wonderous bold;
He said there was never a cuckold’s kniffe
Carve itt that co’ld.
XXXV
Some rubb’d their knives
Uppon a whetstone;
Some threw them under the table,
And said they had none.
XXXVI
King Arthur and the child
Stood looking them upon;
All their knives’ edges
Turnèd backe againe.
XXXVII
Craddocke had a litle kniffe
Of iron and of steele;
He birtled[174] the bore’s head](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-69-320.jpg)
![He birtled[174] the bore s head
Wonderous weale,
That every knight in the King’s court
Had a morssell.
XXXVIII
The litle Boy had a horne,
Of red gold that ronge[175];
He said, ‘There was noe cuckolde
Shall drinke of my horne,
But he sho’ld itt sheede[176]
Either behind or beforne.’
XXXIX
Some shedd it on their shoulder
And some on their knee;
He that co’ld not hitt his mouth
Put it in his e’e;
And he that was a cuckold
Every man might him see.
XL
Craddocke wan the horne
And the bore’s head;
His ladye wan the mantle
Unto her meede;
Everye such a lovely ladye
God send her well to speede!
FOOTNOTES:](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-70-320.jpg)
![[156] co’ld = could, knew.
[157] bedone = adorned.
[158] hett = bid.
[159] potener = pouch, purse.
[160] care = bethink him.
[161] bed = bid, offered.
[162] new fangle = capricious.
[163] gaule = gules, red.
[164] wadded = of woad colour, blue.
[165] blee = hue.
[166] can = did.
[167] walker = fuller.
[168] rudd = complexion.
[169] For why = because.
[170] dinne = noise, i. e. ado.
[171] crowt = pucker.
[172] shreeven = shriven, confessed.
[173] werryed = worried.
[174] birtled = brittled, cut up.
[175] ronge = rung, resounded.
[176] sheede = shed, spill.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-71-320.jpg)
![I
Saies, ‘Come here, cuzen Gawaine so gay,
My sisters sonne be yee;
Ffor you shall see one of the fairest round tables
That ever you see with your eye.’
II
Then bespake Lady Queen Guenever,
And these were the words said shee:
‘I know where a round table is, thou noble king,
Is worth thy round table and other such three.
III
‘The trestle that stands under this round table,’ she said,
‘Lowe downe to the mould,
It is worth thy round table, thou worthy king,
Thy halls, and all thy gold.
IV
‘The place where this round table stands in,
[Is fencèd round amaine]
It is worth thy castle, thy gold, thy fee,
And all good Litle Britaine.’
V
‘Where may that table be, lady?’ quoth hee,
‘Or where may all that goodly building be?’
‘You shall it seeke,’ shee says, ‘till you it find;](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-73-320.jpg)
![You shall it seeke, shee says, till you it find;
You shall never gett more of me.’
VI
Then bespake him noble King Arthur
These were the words said hee:
‘I’le make mine avow to God,
And alsoe to the Trinity,
VII
‘I’le never sleepe one night there as I doe another
’Till that round table I see:
Sir Marramiles and Sir Tristeram,
Fellowes that ye shall bee.
VIII
[‘Sir Gawaine and Sir Bredbettle
Be fellowes eke with me,]
Weele be clad in palmers’ weede,
Five palmers we will bee;
IX
‘There is noe outlandish man will us abide,
Nor will us come nye.’
Then they rived[177] east and they rived west,
In many a strange countrỳ.
X
Then they tranckled[178] a litle further,](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-74-320.jpg)
![They saw a battle new sett:
‘Now, by my faith,’ saies noble King Arthur,
[‘These armies be well met.’]
After travelling in many strange lands they arrive at the castle of
King Cornwall, not a great way from home.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-75-320.jpg)
![XI
But when he cam to this [Cornwall castle]
And to the palace gate,
Soe ready was ther a proud portèr,
And met him soone therat.
XII
Shooes of gold the porter had on,
And all his other rayment was unto the same:
‘Now, by my faith,’ saies noble King Arthur,
‘Yonder is a minion swaine.’
XIII
Then bespake noble King Arthur,
These were the words says hee:
‘Come thou hither, thou proud portèr,
I pray thee come hither to me.
XIV
‘I have two poore rings, of my finger,
The better of them I’le give to thee;
Tell who may be lord of this castle,
Or who is lord in this cuntry?’
XV
‘Cornewall King,’ the porter sayes,
‘There is none soe rich as hee;
Neither in christendome, nor yet in heathendom,](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-76-320.jpg)
![Neither in christendome, nor yet in heathendom,
None hath soe much gold as he.’
XVI
And then bespake him noble King Arthur,
These were the words sayes hee:
‘I have two poore rings of my finger,
The better of them I’le give thee,
If thou wilt greete him well, Cornewall King,
And greete him well from me.
XVII
‘Pray him for one night’s lodging and two meales’ meate,
For his love that dyed uppon a tree;
Of one ghesting[179] and two meales’ meate,
For his love that dyed uppon tree.
XVIII
‘Of one ghesting, of two meales’ meate,
For his love that was of virgin borne,
And in the morning that we may scape away,
Either without scath or scorne.’
XIX
Then forth is gone this proud portèr,
As fast as he co’ld hye,
And when he came befor Cornewall King,
He kneelèd downe on his knee.
XX](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-77-320.jpg)
![Sayes, ‘I have beene porter-man at thy gate
This thirty winter and three,
[But there is ffive knights before itt now,
The like I never did see.’]
King Cornwall questioning the strangers, they happen to speak of a
certain shrine of Our Lady, from which he gathers that they have
been in Little Britain. This leads him to question them concerning
King Arthur.](https://image.slidesharecdn.com/21789691-250523213347-5f211fed/85/Cyberphysical-Systems-Foundations-And-Techniques-Uzzal-Sharma-78-320.jpg)
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- 8. Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener (martin@scrivenerpublishing.com) Phillip Carmical (pcarmical@scrivenerpublishing.com)
- 9. Cyber-Physical Systems Foundations and Techniques Edited by Uzzal Sharma, Parma Nand, Jyotir Moy Chatterjee, Vishal Jain, Noor Zaman Jhanjhi and R. Sujatha
- 10. This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or other- wise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley prod- ucts visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant- ability or fitness for a particular purpose. No warranty may be created or extended by sales representa tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further informa tion does not mean that the publisher and authors endorse the information or services the organiza tion, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 978-1-119-83619-3 Cover image: Pixabay.Com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1
- 11. v Contents Preface xv Acknowledgement xix 1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things 1 Ravi Gedam and Surendra Rahamatkar 1.1 Introduction 2 1.2 Background of Industrial Internet of Things 3 1.3 Literature Review 6 1.4 The Proposed Methodology 13 1.5 Experimental Requirements 14 1.6 Conclusion 15 References 16 2 Integration of Big Data Analytics Into Cyber-Physical Systems 19 Nandhini R.S. and Ramanathan L. 2.1 Introduction 19 2.2 Big Data Model for Cyber-Physical System 21 2.2.1 Cyber-Physical System Architecture 22 2.2.2 Big Data Analytics Model 22 2.3 Big Data and Cyber-Physical System Integration 23 2.3.1 Big Data Analytics and Cyber-Physical System 23 2.3.1.1 Integration of CPS With BDA 24 2.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics 24 2.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System 25 2.4 Storage and Communication of Big Data for Cyber-Physical System 26 2.4.1 Big Data Storage for Cyber-Physical System 27 2.4.2 Big Data Communication for Cyber-Physical System 28
- 12. vi Contents 2.5 Big Data Processing in Cyber-Physical System 29 2.5.1 Data Processing 29 2.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing 29 2.5.1.2 Clustering in Big Data 31 2.5.1.3 Clustering in Cyber-Physical System 32 2.5.2 Big Data Analytics 32 2.6 Applications of Big Data for Cyber-Physical System 33 2.6.1 Manufacturing 33 2.6.2 Smart Grids and Smart Cities 34 2.6.3 Healthcare 35 2.6.4 Smart Transportation 35 2.7 Security and Privacy 36 2.8 Conclusion 37 References 38 3 Machine Learning: A Key Towards Smart Cyber-Physical Systems 43 Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur 3.1 Introduction 44 3.2 Different Machine Learning Algorithms 46 3.2.1 Performance Measures for Machine Learning Algorithms 48 3.2.2 Steps to Implement ML Algorithms 49 3.2.3 Various Platforms Available for Implementation 50 3.2.4 Applications of Machine Learning in Electrical Engineering 50 3.3 ML Use-Case in MATLAB 51 3.4 ML Use-Case in Python 56 3.4.1 ML Model Deployment 59 3.5 Conclusion 60 References 60 4 Precise Risk Assessment and Management 63 Ambika N. 4.1 Introduction 64 4.2 Need for Security 65 4.2.1 Confidentiality 65 4.2.2 Integrity 66 4.2.3 Availability 66 4.2.4 Accountability 66 4.2.5 Auditing 67
- 13. Contents vii 4.3 Different Kinds of Attacks 67 4.3.1 Malware 67 4.3.2 Man-in-the Middle Assault 69 4.3.3 Brute Force Assault 69 4.3.4 Distributed Denial of Service 69 4.4 Literature Survey 70 4.5 Proposed Work 75 4.5.1 Objective 75 4.5.2 Notations Used in the Contribution 76 4.5.3 Methodology 76 4.5.4 Simulation and Analysis 78 4.6 Conclusion 80 References 80 5 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment 85 Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja 5.1 Introduction 86 5.2 IoT Components, Layered Architectures, Security Threats 89 5.2.1 IoT Components 89 5.2.2 IoT Layered Architectures 90 5.2.2.1 3-Layer Architecture 91 5.2.2.2 4-Layer Architecture 91 5.2.2.3 5-Layer Architecture 93 5.2.3 Associated Threats in the Layers 93 5.2.3.1 Node Capture 93 5.2.3.2 Playback Attack 93 5.2.3.3 Fake Node Augmentation 93 5.2.3.4 Timing Attack 94 5.2.3.5 Bootstrap Attack 94 5.2.3.6 Jamming Attack 94 5.2.3.7 Kill Command Attack 94 5.2.3.8 Denial-of-Service (DoS) Attack 94 5.2.3.9 Storage Attack 94 5.2.3.10 Exploit Attack 95 5.2.3.11 Man-In-The-Middle (MITM) Attack 95 5.2.3.12 XSS Attack 95 5.2.3.13 Malicious Insider Attack 95
- 14. viii Contents 5.2.3.14 Malwares 95 5.2.3.15 Zero-Day Attack 95 5.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT 97 5.3.1 Taxonomy of DDoS Attacks 99 5.3.1.1 Architectural Model 99 5.3.1.2 Exploited Vulnerability 100 5.3.1.3 Protocol Level 101 5.3.1.4 Degree of Automation 101 5.3.1.5 Scanning Techniques 101 5.3.1.6 Propagation Mechanism 102 5.3.1.7 Impact Over the Victim 102 5.3.1.8 Rate of Attack 103 5.3.1.9 Persistence of Agents 103 5.3.1.10 Validity of Source Address 103 5.3.1.11 Type of Victim 103 5.3.1.12 Attack Traffic Distribution 103 5.3.2 Working Mechanism of DDoS Attack 104 5.4 Existing Solution Mechanisms Against DDoS Over IoT 105 5.4.1 Detection Techniques 105 5.4.2 Prevention Mechanisms 108 5.5 Challenges and Research Directions 113 5.6 Conclusion 115 References 115 6 Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges 123 Bhimavarapu Usharani 6.1 Introduction 124 6.2 Phishing Threats 124 6.2.1 Internet Fraud 124 6.2.1.1 Electronic-Mail Fraud 125 6.2.1.2 Phishing Extortion 126 6.2.1.3 Extortion Fraud 127 6.2.1.4 Social Media Fraud 127 6.2.1.5 Tourism Fraud 128 6.2.1.6 Excise Fraud 129 6.2.2 Phishing 129 6.3 Deep Learning Architectures 131 6.3.1 Convolution Neural Network (CNN) Models 131 6.3.1.1 Recurrent Neural Network 131
- 15. Contents ix 6.3.1.2 Long Short-Term Memory (LSTM) 134 6.4 Related Work 135 6.4.1 Machine Learning Approach 135 6.4.2 Neural Network Approach 136 6.4.3 Deep Learning Approach 138 6.5 Analysis Report 139 6.6 Current Challenges 140 6.6.1 File-Less Malware 140 6.6.2 Crypto Mining 140 6.7 Conclusions 140 References 141 7 Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks 147 Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar 7.1 Introduction 148 7.2 Literature Review 150 7.3 Classification of Attacks 152 7.3.1 The Perception Layer Network Attacks 152 7.3.2 Network Attacks on the Application Control Layer 153 7.3.3 Data Transmission Layer Network Attacks 153 7.3.3.1 Rogue Access Point 154 7.3.3.2 ARP Spoofing 155 7.3.3.3 DNS Spoofing 157 7.3.3.4 mDNS Spoofing 160 7.3.3.5 SSL Stripping 161 7.4 Proposed Algorithm of Detection and Prevention 162 7.4.1 ARP Spoofing 162 7.4.2 Rogue Access Point and SSL Stripping 168 7.4.3 DNS Spoofing 169 7.5 Results and Discussion 173 7.6 Conclusion and Future Scope 173 References 174 8 Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications 179 Saurav S. and Arnab Ghosh 8.1 Introduction 179 8.2 Modeling of Fourth Order Interleaved Boost Converter 181 8.2.1 Introduction to the Topology 181
- 16. x Contents 8.2.2 Modeling of FIBC 182 8.2.2.1 Mode 1 Operation (0 to d1 Ts) 182 8.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts) 184 8.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts) 186 8.2.2.4 Mode 4 Operation (d3 Ts to Ts) 188 8.2.3 Averaging of the Model 190 8.2.4 Small Signal Analysis 190 8.3 Controller Design for FIBC 193 8.3.1 PID Controller 193 8.3.2 Type II Controller 194 8.3.3 Type III Controller 195 8.4 Computational Results 197 8.5 Conclusion 204 References 205 9 Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management 209 Somya Goyal 9.1 Introduction 210 9.1.1 RFID and IoT for Smart Inventory Management 210 9.2 Benefits and Barriers in Implementation of RFID 212 9.2.1 Benefits 213 9.2.1.1 Routine Automation 213 9.2.1.2 Improvement in the Visibility of Assets and Quick Availability 215 9.2.1.3 SCM-Business Benefits 215 9.2.1.4 Automated Lost and Found 216 9.2.1.5 Smart Investment on Inventory 217 9.2.1.6 Automated Patient Tracking 217 9.2.2 Barriers 218 9.2.2.1 RFID May Interfere With Medical Activities 218 9.2.2.2 Extra Maintenance for RFID Tags 218 9.2.2.3 Expense Overhead 218 9.2.2.4 Interoperability Issues 218 9.2.2.5 Security Issues 218 9.3 IoT-Based Inventory Management—Case Studies 218 9.4 Proposed Model for RFID-Based Hospital Management 220 9.5 Conclusion and Future Scope 225 References 226
- 17. Contents xi 10 A Systematic Study of Security of Industrial IoT 229 Ravi Gedam and Surendra Rahamatkar 10.1 Introduction 230 10.2 Overview of Industrial Internet of Things (Smart Manufacturing) 231 10.2.1 Key Enablers in Industry 4.0 233 10.2.2 OPC Unified Architecture (OPC UA) 234 10.3 Industrial Reference Architecture 236 10.3.1 Arrowgead 237 10.3.2 FIWARE 237 10.3.3 Industrial Internet Reference Architecture (IIRA) 238 10.3.4 Kaa IoT Platform 238 10.3.5 Open Connectivity Foundation (OCF) 239 10.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0) 239 10.3.7 ThingsBoard 240 10.3.8 ThingSpeak 240 10.3.9 ThingWorx 240 10.4 FIWARE Generic Enabler (FIWARE GE) 241 10.4.1 Core Context Management GE 241 10.4.2 NGSI Context Data Model 242 10.4.3 IDAS IoT Agents 244 10.4.3.1 IoT Agent-JSON 246 10.4.3.2 IoT Agent-OPC UA 247 10.4.3.3 Context Provider 247 10.4.4 FIWARE for Smart Industry 248 10.5 Discussion 249 10.5.1 Solutions Adopting FIWARE 250 10.5.2 IoT Interoperability Testing 251 10.6 Conclusion 252 References 253 11 Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems 257 Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee 11.1 Introduction 258 11.2 Popular Privacy Design Recommendations 258 11.2.1 Dynamic Authorization 258
- 18. xii Contents 11.2.2 End to End Security 259 11.2.3 Enrollment and Authentication APIs 259 11.2.4 Distributed Authorization 260 11.2.5 Decentralization Authentication 261 11.2.6 Interoperable Privacy Profiles 261 11.3 Current Privacy Challenges in CPS 262 11.4 Privacy Aware Design for CPS 263 11.5 Limitations 265 11.6 Converting Risks of Applying AI Into Advantages 266 11.6.1 Proof of Recognition and De-Anonymization 267 11.6.2 Segregation, Shamefulness, Mistakes 267 11.6.3 Haziness and Bias of Profiling 267 11.6.4 Abuse Arising From Information 267 11.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem 268 11.7 Conclusion and Future Scope 269 References 270 12 Exposing Security and Privacy Issues on Cyber-Physical Systems 273 Keshav Kaushik 12.1 Introduction to Cyber-Physical Systems (CPS) 273 12.2 Cyber-Attacks and Security in CPS 277 12.3 Privacy in CPS 281 12.4 Conclusion & Future Trends in CPS Security 284 References 285 13 Applications of Cyber-Physical Systems 289 Amandeep Kaur and Jyotir Moy Chatterjee 13.1 Introduction 289 13.2 Applications of Cyber-Physical Systems 291 13.2.1 Healthcare 291 13.2.1.1 Related Work 293 13.2.2 Education 295 13.2.2.1 Related Works 295 13.2.3 Agriculture 296 13.2.3.1 Related Work 297 13.2.4 Energy Management 298 13.2.4.1 Related Work 299 13.2.5 Smart Transportation 300 13.2.5.1 Related Work 301
- 19. Contents xiii 13.2.6 Smart Manufacturing 301 13.2.6.1 Related Work 303 13.2.7 Smart Buildings: Smart Cities and Smart Houses 303 13.2.7.1 Related Work 304 13.3 Conclusion 304 References 305 Index 311
- 21. xv Preface Cyber-Physical Systems (CPS) is the interconnection of the virtual or cyber and the physical system. It is realized by combining three well-known technologies namely “Embedded Systems”, “Sensors and Actuators” and “Network and Communication System”. These technologies combine to form a system known as CPS. In CPS the physical process and information processing are so tightly connected that it is hard to distinguish the indi- vidual contribution of each process from the output. Some of the exciting innovations such as autonomous cars, quadcopter, space ships, sophisti- cated medical devices fall under CPS. The scope of CPS is tremendous. In CPS, we can see the applications of various emerging technologies such as artificial intelligence, Internet of Things (IoT), machine learning (ML), deep learning (DL), big data (BD), robotics, quantum technology, etc. Almost in all the sectors whether it is education, health, human resource development, skill improvement, startup strategy, etc., we see an enhance- ment in the quality output, which is because of the emergence of CPS into the field. The CPS is considered the upcoming industry revolution. This book is covering the different aspects associated with the CPS, such as algorithms, application areas, improvement of existing technology to name a few. The book has 13 quality chapters written by experts in their field. The details of each chapter are as follows: Chapter 1 presents a systematic literature review on cyber security threats of the industrial Internet of Things (IIoT). In recent years, the IIoT has become one of the popular technologies among Internet users for transportation, business, education, and communication development. Chapter 2 explains the integration of big data analytics into CPS. The evolving CPS technology advances BD analytics and processing. The con- trol and management of BD are aided by the architecture of CPS with cyber layer, physical layer, and communication layer is designed which not only integrates but also helps CPS in decision-making.
- 22. xvi Preface Chapter 3 deals with the basics of machine learning techniques. Embedding these techniques in a CPS can make the system intelligent and user-friendly. ML aims to develop computer programs, that not only pro- cess the data to generate output, but also gain information from that data simultaneously, to improve its performance in every next run. Chapter 4 presents a precise risk assessment and management strategy. Chapter 5 presents a detailed review on security issues in layered archi- tectures and distributed denial service of attacks over the IoT environment: As a part of evolution, the current trend is the IoT, which brings automa- tion to the next level via connecting the devices through the Internet, and its benefits are tremendous. Meanwhile, the threats and attacks are also evolving and become an unstoppable menace to IoT users and applica- tions. This chapter addresses critical challenges and future research direc- tions concerning IoT security that gives insights to the new researchers in this domain. Chapter 6 presents ML and DL (deep learning) techniques for phish- ing threats and challenges: Internet security threats keep on rising due to the vulnerabilities and numerous attacking techniques. The swindlers who take skills over the vulnerable online services and get admission to the information of genuine people through these virtual features continue to expand. Security should prevent phishing attacks and to offer availabil- ity and confidentiality. The phishing attack using AI is discussed in this chapter. Chapter 7 presents a novel defending and prevention technique for the man in the middle of attacks in cyber-physical networks: Man in the Middle Attack is a type of cyber-attack in which an unauthorized person enters the online network between the two users, avoiding the sight of both users. The scripts developed successfully defended the deployed virtual machines from the Man in the Middle Attacks. The main purpose behind this topic is to make readers beware of cyber-attacks. Chapter 8 presents the fourth-order interleaved Boost Converter with PID, Type II and Type III controller for smart grid applications: Switched- mode power converters are an important component in interfacing renew- able energy sources to smart grids and microgrids. The voltage obtained from power conversion is usually full of ripples. To minimize the ripple in the output, certain topological developments are made. This is made possible by controlling the converters using Type II and Type III control- lers and the results are compared with PID controller. The performance is analyzed and compared in the Simulink environment. The transient and steady-state analysis is done for a better understanding of the system.
- 23. Preface xvii Chapter 9 presents Industry 4.0 in HealthCare IoT for inventory and supply chain management. Industry 4.0 is a setup reality that fulfills various necessities of the clinical field with expansive assessment. Radio Frequency Identification (RFID) advancement does not simply offer the capacity to discover stuff, supplies, and people persistently, but it also gives capable and exact permission to clinical data for prosperity specialists. Chapter 10 presents a systematic literature review on the security aspects of the Industrial IoT. Chapter 11 acts as a readymade guide to researchers who want to know how to lay foundations towards a privacy-aware CPS architecture. Chapter 12 explains the possible privacy and security issues of CPS. Chapter 13 presents a review of the various application of the CPS. Uzzal Sharma, Assam, India Parma Nand, Greater Noida, India Jyotir Moy Chatterjee, Kathmandu, Nepal Vishal Jain, Greater Noida, India Noor Zaman Jhanjhi, Subang Jaya, Malaysia R. Sujatha, Vellore, India April 2022
- 25. xix Acknowledgement I would like to acknowledge the most important people in my life, i.e., my grandfather Late Shri. Gopal Chatterjee, grandmother Late Smt. Subhankori Chatterjee, my father Shri. Aloke Moy Chatterjee, my Late mother Ms. Nomita Chatterjee & my uncle Shri Moni Moy Chatterjee. The is book has been my long-cherished dream which would not have been turned into reality without the support and love of these amazing people. They have continuously encouraged me despite my failure to give them the proper time and attention. I am also grateful to my friends, who have encouraged and blessed this work with their unconditional love and patient. Jyotir Moy Chatterjee Department of IT Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation) Kathmandu, Nepal-44600
- 27. 1 Uzzal Sharma, Parma Nand, Jyotir Moy Chatterjee, Vishal Jain, Noor Zaman Jhanjhi and R. Sujatha (eds.) Cyber-Physical Systems: Foundations and Techniques, (1–18) © 2022 Scrivener Publishing LLC 1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things Ravi Gedam* and Surendra Rahamatkar† Amity University Chhattisgarh, Raipur, India Abstract In recent years, the Industrial Internet of Things (IIoT) has become one of the pop- ular technology among Internet users for transportation, business, education, and communication development. With the rapid adoption of IoT technology, indi- viduals and organizations easily communicate with each other without great effort from the remote location. Although, IoT technology often confronts unautho- rized access to sensitive data, personal safety risks, and different types of attacks. Hence, it is essential to model the IoT technology with proper security measures to cope up with the rapid increase of IoT-enabled devices in the real-time market. In particular, predicting security threats is significant in the Industrial IoT appli- cations due to the huge impact on production, financial loss, or injuries. Also, the heterogeneity of the IoT environment necessitates the inherent analysis to detect or prevent the attacks over the voluminous IoT-generated data. Even though the IoT network employs machine learning and deep learning-based security mech- anisms, the resource constraints create a set-back in the security provisioning especially, in maintaining the trade-off between the IoT devices’ capability and the security level. Hence, in-depth analysis of the IoT data along with the time efficiency is crucial to proactively predict the cyber-threats. Despite this, relearn- ing the new environment from the scratch leads to the time-consuming process in the large-scale IoT environment when there are minor changes in the learning environment while applying the static machine learning or deep learning models. To cope up with this constraint, incrementally updating the learning environment is essential after learning the partially changed environment with the knowledge *Corresponding author: gedam.hemraj@s.amity.edu † Corresponding author: srahamatkar@rpr.amity.edu
- 28. 2 Cyber-Physical Systems of previously learned data. Hence, to provide security to the resource-constrained IoT environment, selecting the potential input data for the incremental learning model and fine-tuning the parameters of the deep learning model for the input data is vital, which assists towards the proactive prediction of the security threats by the time-efficient learning of the dynamically arriving input data. Keywords: Industrial IoT, smart manufacturing, industry 4.0, interoperability, deep learning, incremental learning 1.1 Introduction In recent years, Industrial Internet of Things (IIoT) technology [1] has gained significant attention among the internet users in the real-world with the increased advantage of the ubiquitous connectivity and interac- tion between the physical and cyber worlds. With the enormously inter- connected IoT devices, IIoT devices have been used in various applications such as smart homes, smart cars, smart healthcare, smart agriculture, and smart retail. The exponential rise of IoT technology often confronts secu- rity and privacy concerns [2]. Nowadays, cyber-attacks such as ransom- ware and malware have increasingly targeted IoT applications to impact the distributed network. Even though the existing security measures are adopted in the IoT environment, IIoT applications are still vulnerable to different attacks due to the massive attack surface [3, 4]. Hence, it is essen- tial to design the defense mechanisms to detect and predict the attacks in the IIoT platform. Applying the traditional security models or mecha- nisms is inadequate for the IIoT environment due to the intrinsic resource and computational constraints. Intrusion detection models dynamically monitor abnormal behaviors or patterns in the system to detect malicious activity. The existing intrusion detection researches have mainly focused on rule-based detection techniques, which lack to support the detection of anomalies in the emerging IIoT platform [5]. To detect anomalies with- out false alarms, artificial intelligence methods have been widely used by security researchers. For the most part, in order to deal with the massive amount of data generated by IoT devices, machine learning and deep learning algorithms have been used to perform automated data analysis as well as to provide meaningful interpretations [6, 7]. Several research works have employed machine learning and deep learning techniques to detect malicious activity in the IIoT environment. Despite the combination of intrusion detection and artificial intelligence-based research, it still con- fronts the precise detection of anomalies in IIoT networks.
- 29. Cyber Security Threats of Industrial Internet of Things 3 Owing to the dynamic arrival of the new malware classes and instances in the IIoT platform, traditional machine learning, and deep learning-based security models deal with the catastrophic forgetting prob- lems. Catastrophic forgetting is the ignorance of the knowledge about pre- vious significant classes while performing the classification for the new classes. The security experts have widely utilized incremental learning models [8, 9]. The incremental learning model continuously learns the new data with the knowledge of the previous learning results. It plays a significant role in improving the detection or prediction performance in developing the security models for the detection of known and unknown attacks. The incremental learning model often confronts the stability- plasticity problem: previous data retaining and new data preserving [10]. Hence, harvesting useful insights from the enormous amount of data are crucial to improve the learning performance. In essence, preprocessing the continuously arriving data streams to augment the training data is crucial for the incremental learning model. Thus, this work focuses on modeling the security mechanism for the IIoT application with the contextual pre- processing and the enhanced deep incremental learning model. With the target of improving the detection performance, it employs the incremental feature selection with optimization for the contextual preprocessing and fine-tunes the learning parameters for the proactive prediction of the mali- cious activities in the IIoT environment. 1.2 Background of Industrial Internet of Things The Fourth Industrial Revolution (4.0) paradigm can be thought of as a road map that takes us through the four industrial revolutions in the development of manual-to-market industrial production processes. Figure 1.1 illustrates the process of creation. With the beginning of the First Industrial Revolution in the 1800s came the development of mechaniza- tion and electric power generation [11]. When mechanical and mechanical power were introduced in the 1800s, the very first Industrial Revolution was launched (Figure 1.2). This resulted in the transition away from phys- ical labor toward the very first methods of production, which was partic- ularly noticeable in the textile industry [12]. The improved overall quality of life played a significant role in the transition process, according to the researchers. Because of the electrification of the world, millions of peo- ple were able to industrialize and develop, sparking the Second Industrial Revolution [13]. To illustrate this point, consider the following quote from Henry Ford, which refers to the Ford T-Model automobile: “You can have
- 30. 4 Cyber-Physical Systems any colour as long as it is black.” Although mass production is becoming increasingly popular, there is still room for product customization if mass production is not used. It is the third industrial revolution, which began with the introduction of microelectronics and automation and has con- tinued to the present day [14]. Module manufacturing is encouraged as a result of this, in which a variety of items is created on flexible production lines by employing programmable machines as well as various materials [15]. These manufacturing processes, on the other hand, are limited in their ability to accommodate varying output volumes, which is a disadvantage. The fourth industrial revolution has begun as a result of the advancement of information and communications technology (ICT). Intelligent auto- mation of cyber-physical systems with decentralized control and advanced networking is the technological foundation for artificial intelligence-based systems. Intelligent automation of cyber-physical systems with decentral- ized control and advanced networking is based on decentralized control Industry 1.0 Industry 2.0 Industry 3.0 Industry 4.0 IoT, Cyber Security 1969 Automation, Computer, Electronics 1870 Mass Production, Electrical energy 1784 Mechanization, Steam power, Weaving loom Figure 1.2 The industrial revolutions. Lightweight Security Solution Parameter Updating for new data Noise – less Augmented training data Figure 1.1 Challenges in artificial intelligence-based IIoT security model.
- 31. Cyber Security Threats of Industrial Internet of Things 5 and advanced networking (IoT functionalities) [25, 26]. A self-organizing cyber-physical production structure was created by reorienting this new industrial production technology using classical hierarchical automa- tion systems. As a result of this new manufacturing technology, scalable mass-customized production as well as flexibility in terms of production volume are now possible. Research Gap The existing security researchers have handled the different types of attacks on the IIoT network by adopting the deep learning and incremental learn- ing models; however, the incremental learning-based security models have been confronted with several shortcomings particularly, in the IIoT net- work, which are discussed as follows. • Applying the available existing IIoT security solutions is crit- ical due to the primary concern of the resource constraints in the IIoT network. • Owing to the need for cross-layer design and optimization algorithms for the security mechanisms, the available secu- rity solutions are inappropriate for the IIoT model. • The DDoS or intrusion detection models often confront the increased probability of false positives, leading to ineffective attack detection [16]. • Lack of modification in the machine learning model while adopting the security solution leads to an increased number of false positives and true negatives. • Traditional deep learning models lack the development of a reliable, robust, and intelligent security mechanism over the massive scale deployment of the IIoT. • Static machine learning and deep learning models lead to inaccurate decision-making due to the continuously arriv- ing data streams from different IIoT data sources [17]. • Incrementally identifying the potential features and making the decisions from the extracted set of features over the con- tinuously arriving data streams is critical. • Traditional preprocessing methods lack to support the effec- tive incremental learning results due to the variations in the inherent relationships of the arriving data [18]. • Incremental learning models lead to inaccurate decision-making without handling the drift data in the
- 32. 6 Cyber-Physical Systems IIoT applications due to the enormous availability of the continuously changing data. • Modeling the deep learning algorithm with the appropriate parameter values is quite critical for detecting known and unknown attacks in the dynamic IIoT environment. Challenges in IIoT Security In the real-world, the IIoT applications often demand both the speed and accuracy ensured data stream mining methods. The IIoT platform con- fronts major security issues due to the ever-increasing complexity of the attacks, zero-day vulnerabilities, the nature of connected IIoT devices, and the lack of detection of new threats. The existing IIoT security models lack in providing suitable security solutions over the continuous arrival of the IIoT data. Owing to the resource-constrained IIoT environment, model- ing the heavy-weight security solution is inappropriate. Even though tradi- tional machine learning and deep learning techniques have been adopted to model the IIoT security solutions, effectively detecting over the contin- uously arriving IIoT data and developing the lightweight security solution is challenging [19]. The continuous arrival of IIoT data leads to the inac- curate detection or classification of the malicious activities due to the exis- tence of the noisy data, which also leads to the increased computational time. Besides, detecting the new malware or attacks in the IIoT environ- ment with a large number of training samples by the traditional learning model is ineffective [20]. To overcome this obstacle, the incremental learn- ing models have been utilized by the IIoT security researchers. However, training the massive amount of arriving data streams and detecting both the known and unknown malware without selecting the potential fea- tures is critical. Hence, there is an essential need to preprocess the massive data streams and protect the IIoT environment from both the known and unknown malware-based attacks [21]. 1.3 Literature Review Several progressive and online algorithms have been written, mostly adapting the existing batch techniques to the progressive environment. Massive theoretical work was done in the stationary environment to test their capacity for generalization and convergence speed, often followed by assumptions such as the linear details. While progress and online learning are well developed and well founded, some publications are only generally
- 33. Cyber Security Threats of Industrial Internet of Things 7 aimed at the elder, especially in the context of big data or the Internet of Things technology. Most of these are surveys that classify available meth- ods and certain fields of application. The principle of progressive learning with a certain motivation for incre- mental learning is included in Giraud-Carrier and Christophe [15]. They promote progressive learning approaches to incremental projects and also illustrate problems such as e-effects ordering or a trustworthiness query. Gepperth and Hammer recently conducted a survey. Usually, the num- ber of measurements and the number of incoming data instances can be approximated. It can also be presumed how critical the rapid response of the system is. It can also be guessed if a linear classifier is suitable for such tasks. Challenges in the Environment An overview of commonly used algorithms with relevant implementation of the real world is also given see Table 1.1. Incremental learning is done more broadly in streaming environments, but much of the work is geared towards drifting ideas. Main Properties for Incremental Algorithms for Domingos and Hulten To sustain the increasingly growing data rate, production, they emphasize the importance of combining models with theoretical performance guar- antees, which are strictly limited in time and space processing. Batch-incremental methods were contrasted and evaluated with examples-incremental methods. The inference is, for example, that incre- mental algorithms are equally effective, but use less energy and that the lazy strategies function especially well with a slider. Fernandez et al. conducted a big test of 179 batch classes on 121 datasets. This comprehensive analysis also included several implementations trendy various toolboxes and languages. The best results were achieved with the Random Forest algorithm [24] and the Gaussian supporting kernel vector Machine (SVM) [25]. However, for incremental algorithms such work is still desperately missing. In this chapter, we take a qualitative approach and examine in depth the main approaches in stationary settings, instead of a broad comparison. We also track the complexity of the model, which takes time and space to draw the required resources, in addition to accu- racy. Our analysis ends with some unknown considerations, such as con- vergence speed and HPO. In machine learning, deep learning is a subfield that is concerned with learning a hierarchy of data inputs. Many areas such as image detection, speech recognition, signal processing, and natural language processing
- 35. Cyber Security Threats of Industrial Internet of Things 9 Table 1.1 Comparison charts. (Continued) Author name and year Methodology Techniques Security type Application area Limitations Qiu, H., et al. (2020) Eliminates the adversarial perturbations by utilizing the pixel drop operation and employs the sparse signal recovery method and wavelet-based denoising method Deep neural network Adversarial attacks Image classification in smart applications Lack of consideration on the parameter tuning leads to inaccurate detection over the dynamic data Parra, G.D.L.T., et al. (2020) Detects the URL attacks, SQL injection, phishing, and DDoS attacks in the IoT through cloud-based distributed deep learning Convolutional neural network and Long short-term memory Phishing and Botnet attacks IoT applications Training the massively arriving input data leads to time inefficiency (Continued)
- 37. Cyber Security Threats of Industrial Internet of Things 11 Table 1.1 Comparison charts. (Continued) Author name and year Methodology Techniques Security type Application area Limitations Reis, L.H.A., et al. (2020) Integrates the incremental learning and unsupervised learning and detects the threats that affect the control loops in the plant One-class support vector machine Zero-day attacks and threats Water treatment plants Fails to reduce the false positive rate Li, J., et al. (2020) Performs opcode sequence extraction and selection to detect malware samples Multiclass support vector machine Known and unknown malware Information security in small scale data Fails to support the large-scale imbalanced data Zhao, W., et al. (2020) Identifies the changes in the flight operations by detecting the outliers through incremental clustering Gaussian Mixture Model and Expectation- maximization algorithm Anomaly detection Flight Security Fails to assign the number of clusters and fails to update the parameters
- 38. 12 Cyber-Physical Systems have now been enriched by deep learning algorithms, which have been learned by researchers in order to solve problems. Deep learning methods are a category of learning methods that can hierarchically learn characteristics from the lower to higher level by constructing a deep architecture. The deep learning methods are able to learn features on several levels automatically, which enable the algorithm to learn complex mapping functions directly from data without human characteristics. The key characteristic of profound methods of learning is that their models are all profoundly architectured. A deep architecture means that the network has many secret layers. A shallow architecture, in comparison, has only few hidden layers (one to two layers). Deep neural networks are effectively implemented in different fields: regression, classification, size reduction, movement modeling, texture modeling, information retrieval, processing of natural languages, robotics, error diagnosis and road cracks. In the ML model, a set of 21 feed profound neural networks was created, which included a variety of DNN values, such as the number of hidden lay- ers, the number of processing units per layer, the triggering of functions, and methods of optimization and regulation. The permutation method [22] has been used to determine the relative value in the ensemble’s accu- racy of the various biochemical markers. Standardization batch [23] was used to minimize overfit effects and improve the stability of the model’s convergence.The best results were obtained by using a DNN with five hid- den layers and the regularised mean squared error (MES) function for loss estimation in the loss estimation, the activation PReLU function (PReLU) [24] for each layer and the loss optimization AdaGrad [25] for each layer. The highest DNN score with 82% accuracy was β = 10, i.e. when the pre- dicted age was ±10 years of true age, it found the sample to be correctly accepted, exceeding many groups of the competing ML models. Several models were evaluated for the combination of each DNN into an ensemble (stacking), and the elastic net model was most successful [26]. Albumin, glucose, alkaline phosphatase, urea and erythrocyte have been the most effective blood markers. This model should be incremental learning as well deep learning in industrial IoT.
- 39. Cyber Security Threats of Industrial Internet of Things 13 1.4 The Proposed Methodology In recent years, the Industrial Internet of Things (IIoT) has become a pop- ular technology among Internet users for transportation, business, educa- tion, and communication development. With the rapid adoption of IIoT technology, individuals and organizations easily communicate with each other without great effort from the remote location. However, the IIoT technology often confronts the unauthorized access of sensitive data, per- sonal safety risks, and different types of attacks. Hence, it is essential to model the IIoT technology with proper security measures to cope with the rapid increase of IIoT-enabled devices in the real-time market. In particu- lar, predicting security threats is significant in the Industrial IIoT applica- tions due to the huge impact on production, financial loss, or injuries. Also, the heterogeneity of the IIoT environment necessitates the inherent anal- ysis to detect or prevent the attacks over the voluminous IIoT-generated data. Even though the IIoT network employs machine learning and deep learning-based security mechanisms, the resource constraints create a set- back in the security provisioning especially, in maintaining the trade-off between the IIoT device’s capability and the security level. Hence, in-depth analysis of the IIoT data along with the time efficiency is crucial to predict the cyber-threats proactively. Despite, relearning the new environment from scratch leads to the time-consuming process in the large-scale IIoT environment when there are minor changes in the learning environment while applying the static machine learning or deep learning models. To cope with this constraint, incrementally updating the learning environ- ment is essential after learning the partially changed environment with the knowledge of previously learned data. Hence, to provide security to the resource-constrained IIoT environment, selecting the potential input data for the incremental learning model and fine-tuning the parameters of the deep learning model for the input data is vital, which assists towards the proactive prediction of the security threats by the time-efficient learning of the dynamically arriving input data. Figure 1.3 illustrates the processes involved in the proposed IIoT secu- rity methodology. The proposed approach incorporates the contextual pre- processing and the proactive prediction processes with the help of the deep incremental learning model and the optimization method. Initially, to effec- tively clean the continuously arriving data streams, the proposed approach explores the noisy and misclassified instances in the arrival of data and then incrementally selects the features within a particular timeframe based on the impact on the classification performance. In subsequence, it optimizes
- 40. 14 Cyber-Physical Systems the feature selection process through the heuristic search strategy that tar- gets improving the time efficiency in the attack detection process. Moreover, it assists in augmenting training data generation with the optimal features alone, which leverages the improved classification performance. The pro- posed approach applies the deep incremental learning model with the fine-tuning of the learning parameters for the input data in the IIoT envi- ronment. The adaptive updating of the learning parameters associated deep incremental learning model ensures the classification or prediction of the malicious instances in the IIoT platform based on the learning knowledge from the augmented training set. Thus, the proposed approach effectively protects the IIoT environment with improved time efficiency with the help of the deep incremental learning model along with the heuristic model. 1.5 Experimental Requirements It is necessary to have an i7 processor with 32 GB or extended memory and a 500 GB hard drive in order to run the experimental framework on Generated AugmentedTraining Set IIoT Devices Noisy and Misclassified Instance Removal Deep Incremental Learning Based Prediction Feature Selection Using Incremental Learning Feature Selection optimization through Heuristic Search Timeframe-Based incremental feature extraction Optimal Parameter Selection for learning model Adaptively assigning for learning parameters Incrementally learning the augmented training set Threats Classification and Prediction Malicious & Benign Instances Figure 1.3 Deep incremental learning-based IIoT security model.
- 41. Cyber Security Threats of Industrial Internet of Things 15 Ubuntu 18.04 LTE. The experimental model makes use of the IIoT data- set, which combines the normal data with the data collected during the attack release. Furthermore, in order to run the deep incremental learning algorithm, the experimental framework makes use of the python libraries, which are running on the Python 3.6.8 platform. Evaluation Metrics Detection Rate: It is the ratio of the number of correctly detected attacks to the total number of attacks in the IIoT environment. It is also termed as the recall. Accuracy: It measures the overall detection accuracy of the IIoT secu- rity model, which considers the accurate detection performance on both the attacks and normal activities. Both true positive and true negative refer to the number of malicious activities that were correctly classified or predicted as attacks, as well as the number of normal activities that were correctly classified or pre- dicted as normal. A false positive represents a malicious activity that was incorrectly classified or predicted as normal, while a false negative rep- resents a legitimate activity that was incorrectly classified or predicted as an attack. 1.6 Conclusion This work presented the incremental learning-based security model for the IIoT environment. The proposed IIoT security mechanism has focused on the classification and prediction of the cyber threats through contextual preprocessing and the deep incremental learning-based prediction. With the target of proactively predicting the malicious instances or activities in the IIoT, this work has outlined the processes of the generation of the aug- mented training set for the deep increment learning model. The contextual preprocessing involves removing the noisy and misclassified instances, incremental feature selection, and heuristic search-based feature selection optimization. The deep incremental learning-based prediction involves the optimal and adaptive learning parameters selection, learning the aug- mented training data with the fine-tuned values, and incremental classifi- cation and prediction. Thus, the proposed security mechanism proactively protects the IIoT environment from malicious activities through the light- weight and time-efficient intelligence model.
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- 45. 19 Uzzal Sharma, Parma Nand, Jyotir Moy Chatterjee, Vishal Jain, Noor Zaman Jhanjhi and R. Sujatha (eds.) Cyber-Physical Systems: Foundations and Techniques, (19–42) © 2022 Scrivener Publishing LLC 2 Integration of Big Data Analytics Into Cyber-Physical Systems Nandhini R.S.* and Ramanathan L. Computer Science and Engineering, Vellore Institute of Technology, Vellore, India Abstract The evolving Cyber-Physical Systems technology advances the big data analytics and processing. The chapter discusses the topics of Big Data which are required for Cyber-Physical Systems across all data streams including the heterogeneous data resource integration. The challenges such as integration of data generated from multiple sources into cyber-physical systems, big data for conventional data- bases and offline processing, scalability are further considered. The control and management of big data is aided by the architecture of cyber-physical system with cyber layer, physical layer and communication layer is designed which not only integrates but also helps cyber-physical system in decision making. The case study that aids big data processing and analytics in cyber-physical system is stated. Keywords: Cyber-Physical systems, big data analytics and processing, Internet of Things, data mining 2.1 Introduction The rapid growth in things or devices in particular sensors and actuators made the development to control the smart physical things, smart objects and digital technologies such as machines in smart manufacturing and structures in smart cities, etc. possible. The communication technolo- gies and physical devices are merged to generate systems that are effec- tive, productive, safe called intelligent systems, where the integrations and interactions are combined to create a global cyber-physical system. *Corresponding author: rameshsneka.nandhini@vit.ac.in
- 46. 20 Cyber-Physical Systems A cyber-physical system is the association of cyber and physical com- ponents that have been specifically engineered to monitor, coordinate and control based on computational algorithms. It is a 3C technology— communication, computation, and control. Cyber-physical systems capture the data from the wireless sensor devices and monitor them, the control of the physical devices is based on the physical data using actua- tors, thus interacting both with the physical and cyber world in the real environment. These systems are interconnected with each other on a uni- versal scale using different network and communication resources. The physical control is efficient when the data collected from the sensors are processed for information with data mining techniques. The interaction among the users from context perspective, the physical device’s surround- ings and the process in the cyber-physical systems are observed when the features of cyber-physical system are considered. However, the integration rules, interoperation among the devices, control of cyber-physical system are the functions that are globally distributed and networked in real-time [1]. This system is used extensively in many applications such as industries, transport and vehicular industry, medical and health management, smart grids, military applications, weather forecasting and many more. An enormous measure of data is generated from various digital tech- nologies like wireless detectors and sensors, mobile phones, storage devices connected to the internet where a continuous data stream is pro- duced. Cyber-physical system has a computational capability that needs to be scaled to provide efficiency as the increasing sensors, digital tech- nologies and devices that are networked create a huge volume of data. To develop a system that is more efficient, intelligent, reliable, trustworthy and self-adaptable integration of big data into a cyber-physical system is manda- tory. Computing and computational resources are comparatively lower than the huge data generated from various resources. The big data analytics tech- niques aim to examine, process and handle the big data characteristics of data to identify the patterns, obtain the information that is needed and rela- tionships in the data sets also the innovative forms of data can be obtained for decision making and process control. The insights about how to model, capture, specify, transfer, organize and manage the data efficiently can be discovered [2]. Conventional data analytics processes the data sets the whole size or type, whereas big data analytics collect, process the data and manage them with low latency and typical data such as unorganized data, data gathered from the sensors including the ones that have spatiotemporal characteristics and the data produced in real-time considered as the stream of data flow can be composed with faster results during real-time process- ing. Machine learning (ML), artificial neural networks (ANN), statistics,
- 47. Big Data Analytics in Cyber-Physical Systems 21 dynamic Bayesian networks (DBA), deep learning, and natural language processing are some of the advanced big data techniques. The merging the big data analytics with the cyber-physical system is inevitable as it is the key to productive, efficient and adaptable cyber-physical system to sustain. The following are the contents discussed in the chapter. Section 2.2 con- tains the architecture of cyber-physical system from a big data model for cyber-physical system. Section 2.3 explains the issues and challenges when big data is integrated with cyber-physical system, integration of CPS and BDA and its control and management. The storage and its communication of big data for cyber-physical system are stated in Section 2.4. Data pro- cessing techniques and models of big data such as cloud and multi-cloud processing, clustering in big data and cyber-physical system and big data analytics models are stated in Section 2.5. Applications of big data-enabled CPS are stated in Section 2.6 particularly manufacturing, smart grids and smart cities, healthcare and smart transportation. The data security and privacy from the CPS applications and loop holes that cause cyber threats in big data analytics are further discussed. 2.2 Big Data Model for Cyber-Physical System Thebigdatacharacteristicscanbeunderstoodby5Varchitecture—volume, variety, veracity, velocity and value [3]. Big data analytics (BDA) is applied in many distinct domains such as e-commerce, enterprise to predict the patterns of customers’ interest, and weather forecasting, where changes in the weather can be analyzed and pattern prediction is done based on past data, etc. The data characteristics are varied and the implementation of aggregated data cost is considered due to which smart data was pro- posed. The concept of smart data is to make sure to eliminate the noise so that important and relevant data can be obtained, which can further be used for application purpose in cyber-physical system to monitor and control so that accurate decision can be made which impacts the physical device in the real-time environment [4]. The present BDA models that are used focus on mining the data, functions that process the data along with data storage and visualization instead of exploring the ways that big data acquire smart data from raw data which makes the integration vulnerable and lowering the analytic capabilities of the system. The BDA architecture should improve the effectiveness and intelligence of the cyber-physical sys- tem. The communication layer is included in the system architecture for smart data purpose, data source layer is included in the BDA model which integrates smart methods for data mining and visualizing layer that aids in
- 48. 22 Cyber-Physical Systems the integration of collection, pre-processing, storage, mining and visual- ization of data functions in CPS [5]. 2.2.1 Cyber-Physical System Architecture The BDA enabled CPS design comprises of three layers namely—a physical layer, a cyber layer and a communication layer. Physical layer—Sensors that are locally distributed across the CPS appli- cation fields generate data that are accumulated in the layer for further process. This data contains noise and are uncertain which can be termed as raw data and needs to be processed. Communication layer—This layer pre-processes the raw data into smart data and converts the decisions from the cyber layer to executable commands. Cyber layer—Controlling and monitoring decisions are made by analysing the data that reflects in the infrastructure of the physical layer. State sensing, intelligent analysis in real-time, accurate execution and self-optimization are some of the main functions of the architecture from a data processing perspective. 2.2.2 Big Data Analytics Model The BDA is the other section of the architecture—a vast amount of raw data is processed so that decisions are made faster and better. The learn- ing process in the BDA model is inspired by the human brain, techniques (support vector machine, fuzzy clustering, convolutional neural networks, auto-encoders, deep learning models) that are integrated with data pro- cessing techniques [6]. The big data analytics model contains four layers— the data source layer, smart data warehouse layer, smart data mining layer and smart visualization layer. Data source layer—Many technologies are used to gather data in this layer. Raw data is collected from distributed wireless sensors that include industrial applications, social media, the internet, etc. from the physical CPS devices. Smart data warehouse layer—This layer manages and maintains histori- cal data that aids decision making and provides an environment to anal- yse information [7]. The raw data is processed into information with the aid of a data cleaning module that removes the inaccurate record, a data
- 49. Big Data Analytics in Cyber-Physical Systems 23 integration module that integrates data with different formats, a data reduction module that reduces data to a more simplified form, data trans- formation module converts raw data to same formats and data discretiza- tion module that converts attributes to discrete intervals. Smart data mining layer—This layer consists of five modules—extraction model, training model, analytic model, data mining model, and prediction model. Different BDA techniques are used in each model for better results. Smart data visualization model—This layer can be designed according to users’ preferences. The analytic results are displayed to gain perception into the modelled data through visualization techniques. 2.3 Big Data and Cyber-Physical System Integration Big data analytics is necessary for cyber-physical system as it produces a mas- sive amount of data dynamically, which needs to be explored and examined to obtain useful information and predict patterns. It is undoubtedly proven that the integration of BDA into CPS is inevitable. The big data-enabled CPS must process all the complex data to ensure that the correct operation is car- ried out so that the system can make the decision and control the dynamic continuous changing behavior of the physical devices. To implement the big data-enabled CPS many concepts are to be adapted and introduced such as data structures, big data features and characteristics and spatial and tem- poral constraints. However, this integration does not fit the offline process- ing data solutions which are conventional as the system deals with the real world where the decisions made are critical and takes place in the real-time. The consequences of big data in real-time need to be resolved by a suitable non-classic, vertically integrated solution that handles real-time stream pro- cessing for control purposes and batch processing for learning purposes. 2.3.1 Big Data Analytics and Cyber-Physical System Integrating the cyber-physical system with big data analytics, the CPS focuses on the streaming data produced by the sensors and the data analytics part, where the computation and communication systems collect the data. The fea- tures of big data need to be considered in the integration process where the Volume estimates the total amount of data volume, Velocity determines the pace with which the data is created and aggregated, Variety tells the richness in the data representation, and Value estimates the information from the raw
- 50. 24 Cyber-Physical Systems data to make decisions. Apart from this, spatial data is also taken into account as it plays important part in the big data-enabled cyber-physical systems. 2.3.1.1 Integration of CPS With BDA To enable the integration of two systems, an Architecture Analysis and Design Language (AADL) [8], Modelica modeling language—Modelicaml [9] and clock theory [10] integration ensures that the requirements of big data are met and are implemented on the platforms of big data and its prop- erties are considered [11]. A vector-logical big data processing approach, that lets cyber-physical systems control the operations and a computing automation model that impacts performance and hardware intricacy is proposed in the aid of the integration [12]. 2.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics The so-called system controls the interconnected devices and systems between the physical environment and the computational capabilities in a real-time dynamic environment and manages them. Self-awareness, self-configuration and self-repairing are some of the abilities that cyber-physical system has to adapt for the system to sustain. The big data environment handles the data as a service to deal with, where this service will be able to manage big data characteristics such as volume, velocity and variety while gathering the generated data from the sensors and the machine controls, and organize them based on the multi-dimensional feature spaces and apply in the industry 4.0 to function [13]. Some of the challenges here faced are big data acquisition and storage, widespread data relevance, data stream elaboration, analysing the data and machine similarity identification, the human–machine interfaces (HMI) based on certain applications and feedback-control mechanisms. Managing and control of cyber-physical system always depend on the modes created by the humans, but hard to verify and maintain as they are incomplete which leads us to data-driven approaches where the huge amount of data collected by the CPS are modeled such that they learn auto- matically the models. Cognitive reference architecture is best preferred in this context [14]. This analysis of cyber-physical systems includes different interfaces that interconnect with each other. The big data platform is an interface that all the relevant raw data from the machines and sensors are gathered and prepared for analysis and interpretation. The next interface is
- 51. Big Data Analytics in Cyber-Physical Systems 25 learning algorithms that brief about the anomaly detection used for mon- itoring conditions and predictive maintenance from the data. The infor- mation provided from the learning algorithm interface is combined with specific domain knowledge to identify faults and semantic context is added to the results in this conceptual layer. The results from the conceptual layer are converted in a human-understandable manner and implemented to achieve better standardization, efficiency and repeatability in task-specific HMI. Another conceptual layer is placed where the use of knowledge is done to recognize actions that are needed to be taken under the users’ deci- sions which are needed to be communicated to the next interface. The final interface is the adaption layer where the computation of commands takes place in real-time, which communicates changes to the control system that reflects in the physical device. Modeling the cyber-physical with big data should consider the chaotic features caused by the control of cyber-physical system as it deals with the vast amount of data and its control so that it may lead to unpredicted results. The cyber-physical system responds to all the minor changes and disturbances which cause the system to be sensitive. A fuzzy feedback lin- earization model followed by a time prediction algorithm is initiated to tackle the chaotic control problems in CPS and also including the synchro- nization control problem [15]. 2.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System The big data-driven CPS will consider the special characteristics and attri- butes, restrictions, demands and constraints along with the basic big data properties—volume, velocity, variety, volatility, value, veracity and validity that are met during the development of certain system domain integrated with big data. The functional components of big data in CPS are system infrastructure and data analytics which should be considered during the integration. Real-time communication between the physical and cyber devices, where capturing the data, monitoring the database and its func- tionalities and the distributed computing is part of the system infrastruc- ture component. Data analytics deals with product actualization and resource efficiency and organization along with predictive and descriptive analysis. Some other important issues that deal with both the components are adaptability, flexibility, security and reliability. In cyber-physical system, a vast amount of data from networking sen- sors, machines, and several other embedded devices are collected from
- 52. 26 Cyber-Physical Systems the physical environment. These data-producing devices such as sensors are not restricted to a certain time or space and also several category and forms such as temperature, speed, geographical data, environmental data, astronomical data, health and logistics data from different sectors and also from digital equipment, transportation and public facilities and smart homes. This leads us to spatiotemporal data requirements, where the sys- tem mostly functions in a real-time environment which makes us consider the spatial and temporal data. Geographical data, time-series data, data from remote locations and from moving object trajectories—where data contains movement history of objects are considered as spatiotemporal data. The time and space correlations are to be considered as important cyber-physical system data features, where the dimensions of such data are observed during analysis and processing. The heterogeneous data are most common in cyber-physical system and the data representation and model makes the data more insightful. Real-time support, sensing and communication services availability, maintenance, infrastructure for the system, evolvability, modularity challenges are persistent when the inte- gration takes place. This integration also questions the infrastructure of the cyber-physical system where the communication and computational capa- bilities needed to be inspected. Security is another important challenge as its standards vary from applications when they interact with different devices. The control decisions, the trustworthiness of data and authentica- tion of devices and their management where there is a necessity to inter- pret the protocols and approach towards the system in specific applications as security demands [16]. 2.4 Storage and Communication of Big Data for Cyber-Physical System The management of data and communication in the real environment is key for a successful system to function and sustain constantly with effi- ciency. Managing the storage operation for cyber-physical system with big data solutions should be regarded alongside caching and routing as there is a huge amount of traffic from the social media applications, people health data, traffic and weather monitoring applications and other smart home appliances which led to the researchers find solutions in storage and com- munications of big data CPS. Enhancing the performance of system needs
- 53. Big Data Analytics in Cyber-Physical Systems 27 to concentrate on the improvement of data collection, data processing techniques from a storage perspective. 2.4.1 Big Data Storage for Cyber-Physical System Storing the persistent and continual data from numerous resources demands that the approaches be efficient and effective from a scalability, cost and flexibility perspective. Combining the cloud/edge computing facilities with big data analytics can give significant results for data storage objectives. Innovative measures should be applied such as proactive con- tent caching in the networks and its characteristics that predict the user behaviour is the motivation for big data-enabled architectures where data and statistical analysis and visualizations methods are taken into account at base stations. To satisfy the users the data is controlled and used for con- tent popularity estimation and content caching in which cyber-physical system has a high interest [17]. Pre-cache technologies are used with big data for higher performances during the transfer of data from sensors to servers, given that cyber- physical system generates a vast amount of data, where network traffics are caused. Two differential algorithms namely Data Filter Algorithm (DFA) and Data Assembler on Server Algorithm (DASA) are used to reduce the traffic in the networks during the data transfer [18]. This can be implied as an optimal trade-off solution that resolves the network traffic problem effectively and also the data accuracy problem where the data captured by the sensors are changed slightly due to the accuracy of the sensors. The data accuracy is dealt with by choosing the relevant parameters and the algorithm functions before sending the data to the servers by using filters and places them in the sensors and a measure is assigned to each. Performing the caching on the wireless sensor networks, device-to- device networks in wireless environment and its caching and other data generation devices like base stations rather than on the clouds offers a pos- itive impact on data management. Coded multi transmission is used at the base stations for caching in a realistic environment which allows sharp attributes and quality of the throughput in the asymptotic regime of the sensors which is based on a simple protocol model that uses geometric link conflict constraints and captures elementary aspects of the interference and spatial spectrum reuse [19]. The integration of big data with real-time CPS finds these caching and storage techniques very useful where reliability and predictability are preferred first and different strategies to enhance the CPS performance can be used to speed up the data collection, processing
- 54. 28 Cyber-Physical Systems and distribution and the correct use of caching techniques makes the sys- tem more manageable. 2.4.2 Big Data Communication for Cyber-Physical System Cyber-physical system makes decisions considering the data generated from the sensors newly created by the digital technologies which provide information and is used for processing. The innovations in big data tech- nologies provide new insights into the effect of strategic communication, the communication process needs to be analyzed and controlled along with the management of information in real-time evaluation. Modern ways of thinking and decision making are one of the prominent promises that big data computing offers. The data is always made available to the users’ advantage so that optimal decisions can be made by determining the latest information which gives more accurate results. Big data delivery technology can be a key technology that does computing better. The big data transmission requirements are to be considered and met among the big data characteristics, which is challenging to process the data where the limited transmission capabilities are to be observed. The big data environment should be made familiar for cyber-physical systems by proposing new architectures, network infrastructure and other services that have become vital. The data delivery performance should be improved for betterment in the device-to-device (D2D) communications. Without support from the network infrastructure or central control units, the data is exchanged among the nodes. There are certain limitations in the data delivery capacity in D2D communications when the quality and mobility of the nodes are considered. As the cognitive radio technology is integrated with D2D communications, the cognitive radio technology gives the device-to-device the ability to improve the data delivery capacity and makes D2D an alternative that acts as supporting system for the appli- cations of big data [20]. The routing algorithms for D2D cognitive radio networks should be appropriately chosen along with its communication. Integrating the wireless sensor network with mobile cloud computing cre- ates significant advantages where WSN have distributed sensors spatially that monitor the physical conditions such as temperature, sound, pres- sure, motion, light etc. that changed the way that interaction takes place with the physical world, whereas mobile cloud computing appears to be the new computing model with efficiency, powerful and unique comput- ing basics such as processors, storage, applications and services offered in networks which can be accessed easily on demand. Lower operating cost, high scalability, easy accessibility and maintenance expense are some of the
- 55. Another Random Document on Scribd Without Any Related Topics
- 56. Her middle ye weel mot[146] span; He’s thrown to her his gay mantle, Says, ‘Lady, hap[147] your lingcan[148].’ VI Her teeth were a’ like teather stakes[149], Her nose like club or mell[150]; An’ I ken naething she ’pear’d to be But the fiend that wons[151] in hell. VII ‘Some meat, some meat, ye King Henry, Some meat ye gie to me!’— ‘An’ what meat’s in this house, ladye, That ye’re not welcome tae?’— ‘O ye’se gae[152] kill your berry-brown steed, And serve him up to me.’ VIII O whan he slew his berry-brown steed, Wow but his heart was sair! She ate him a’ up, skin an’ bane, Left naething but hide an’ hair. IX ‘Mair meat, mair meat, ye King Henry, Mair meat ye gie to me!’— ‘An’ what meat’s in this house, ladye, That ye’re not welcome tae?’— ‘O do ye slay your good grey-hounds
- 57. O do ye slay your good grey hounds An’ bring them a’ to me.’ X O whan he slew his good grey-hounds, Wow but his heart was sair! She ate them a’ up, skin an’ bane, Left naething but hide an’ hair. XI ‘Mair meat, mair meat, ye King Henry, Mair meat ye gie to me!’— ‘An’ what meat’s in this house, ladye, That ye’re not welcome tae?’— ‘O do ye kill your gay goss-hawks An’ bring them a’ to me.’ XII O whan he fell’d his gay goss-hawks, Wow but his heart was sair! She’s ate them a’ up, skin an’ bane, Left naethin’ but feathers bare. XIII ‘Some drink, some drink, now, King Henry, Some drink ye bring to me!’— ‘O what drink’s in this house, ladye, That ye’re not welcome tae?’— ‘O ye sew up your horse’s hide, An’ bring in drink to me.’
- 58. XIV O he’s sew’d up the bluidy hide, A puncheon o’ wine put in; She’s drunk it a’ up at a waught[153], Left na ae drap ahin’[154]. XV ‘A bed, a bed, now King Henry, A bed ye’se mak’ to me!’— ‘An’ what’s the bed in this house, ladye, That ye’re not welcome tae?’— ‘O ye maun pu’ the heather green, An’ mak’ a bed to me.’ XVI Syne pu’d he has the heather green, An’ made to her a bed, An’ up has he ta’en his gay mantle, An’ o’er it he has spread. XVII ‘Tak’ off your claiths now, King Henry, An’ lie down by my side!’— ‘O God forbid,’ says King Henry, ‘That ever the like betide; That ever a fiend that wons in hell Shou’d streak[155] down by my side!’ XVIII
- 59. But whan day was come, and night was gane, An’ the sun shone thro’ the ha’, The fairest ladye that ever was seen [Cam’ to his armès twa]. XIX ‘O weel is me!’ says King Henry, ‘How lang’ll this last wi’ me?’ Then out an’ spake that fair ladye, ‘Even till the day you dee. XX ‘For I’ve met wi’ many a gentle knight That’s gien me sic a fill; But never before wi’ a courteous knight That ga’e me a’ my will.’ FOOTNOTES: [140] routh = plenty. [141] burd-alone = lone as a maid. [142] jelly = jolly, jovial. [143] bierly = stout, handsome. [144] fleer = floor. [145] hat = hit. [146] mot = might. [147] hap = cover. [148] lingcan for lycam = body.
- 60. [149] teather stakes = tether pegs. [150] mell = mallet. [151] wons = dwells. [152] ye’se gae = you shall go. [153] waught = draught. [154] ahin’ = behind. [155] streak = stretch.
- 61. 17. The Boy and the Mantle A Ballad of King Arthur’s Court.
- 62. I In the third day of May To Carleile did come A kind curteous child That co’ld[156] much of wisdome. II A kirtle and a mantle This child had uppon, With brauches and ringes Full richelye bedone[157]. III He had a sute of silke About his middle drawne; Without he co’ld of curtesye He thought it much shame. IV ‘God speed thee, King Arthur, Sitting at thy meate; And the goodly Queene Guenever! I cannot her forget. V ‘I tell you, lords in this hall, I hett[158] you all heed, b h
- 63. Except you be the more surer Is for you to dread.’ VI He pluck’d out of his potener[159], And longer wo’ld not dwell, He pull’d forth a pretty mantle Betweene two nut-shells. VII ‘Have thou here, King Arthur, Have thou here of mee: Give itt to thy comely queene Shapen as itt is alreadye. VIII ‘Itt shall never become that wiffe That hath once done amisse.’ Then every knight in the king’s court Began to care[160] for his. IX Forth came dame Guenever, To the mantle she her bed[161]; The ladye shee was new fangle[162] But yett she was affrayd. X h h h d k h l
- 64. When shee had taken the mantle, She stoode as shee had beene madd; It was from the top to the toe As sheeres had it shread. XI One while was it gaule[163], Another while was itt greene, Another while was it wadded[164]; Ill itt did her beseeme. XII Another while it was blacke, And bore the worst hue: ‘By my troth,’ quoth King Arthur, ‘I thinke thou be not true.’ XIII Shee threw downe the mantle, That bright was of blee[165]; Fast with a rudd red To her chamber can[166] she flee. XIV She cursed the weaver and the walker[167] That cloth that had wrought, And bade a vengeance on his crowne That hither hath itt brought. XV
- 65. XV ‘I had rather be in a wood, Under a greenè tree, Than in King Arthur’s court Shamèd for to bee.’ XVI Kay call’d forth his ladye And bade her come neere; Saies, ‘Madam, and thou be guiltye I pray thee hold thee here.’ XVII Forth came his ladye Shortlye and anon; Boldlye to the mantle Then is she gone. XVIII When she had tane the mantle, And her about it cast Then was she bare All unto the waist. XIX Then every knight That was in the King’s court Talk’d, laugh’d and showted Full oft att that sport.
- 66. XX She threw down the mantle That bright was of blee, Fast with a red rudd[168] To her chamber can she flee. XXI Forth came an old Knight Pattering ore a creede, And he proferr’d to this little Boy Twenty markes to his meede; XXII And all the time of Christmasse Willingly to ffeede; For why[169] this mantle might Doe his wiffe some need. XXIII When shee had tane the mantle Of cloth that was made, Shee had no more left on her But a tassell and a threed: That every knight in the King’s court Bade evill might shee speed. XXIV She threw downe the mantle
- 67. She threw downe the mantle, That bright was of blee, Fast with a red rudd To her chamber can she flee. XXV Craddocke call’d forth his ladye And bade her come in; Saith, ‘Winne this mantle, ladye, With a little dinne[170]. XXVI ‘Winne this mantle, ladye, And it shal be thine If thou never did amisse Since thou wast mine.’ XXVII Forth came Craddocke’s ladye Shortlye and anon, But boldlye to the mantle Then is shee gone. XXVIII When she had tane the mantle And cast it her about, Up at her great toe It began to crinkle and crowt[171]: Shee said, ‘Bowe downe, mantle, And shame me not for nought.
- 68. XXIX ‘Once I did amisse, I tell you certainlye, When Craddocke’s mouth I kist Under a greenè tree; When I kist Craddocke’s mouth Before he marryed mee.’ XXX When shee had her shreeven[172] And her sinnes shee had tolde, The mantle stood about her Right as she wo’ld; XXXI Seemelye of coulour, Glittering like gold Then every knight in Arthur’s court Did her behold. XXXII The little Boy stoode Looking over a dore; [There as he look’d He was ware of a wyld bore.] XXXIII
- 69. XXXIII He was ware of a wyld bore Wo’ld have werryed[173] a man: He pull’d forth a wood-kniffe Fast thither that he ran: He brought in the bore’s head And quitted him like a man. XXXIV He brought in the bore’s head, And was wonderous bold; He said there was never a cuckold’s kniffe Carve itt that co’ld. XXXV Some rubb’d their knives Uppon a whetstone; Some threw them under the table, And said they had none. XXXVI King Arthur and the child Stood looking them upon; All their knives’ edges Turnèd backe againe. XXXVII Craddocke had a litle kniffe Of iron and of steele; He birtled[174] the bore’s head
- 70. He birtled[174] the bore s head Wonderous weale, That every knight in the King’s court Had a morssell. XXXVIII The litle Boy had a horne, Of red gold that ronge[175]; He said, ‘There was noe cuckolde Shall drinke of my horne, But he sho’ld itt sheede[176] Either behind or beforne.’ XXXIX Some shedd it on their shoulder And some on their knee; He that co’ld not hitt his mouth Put it in his e’e; And he that was a cuckold Every man might him see. XL Craddocke wan the horne And the bore’s head; His ladye wan the mantle Unto her meede; Everye such a lovely ladye God send her well to speede! FOOTNOTES:
- 71. [156] co’ld = could, knew. [157] bedone = adorned. [158] hett = bid. [159] potener = pouch, purse. [160] care = bethink him. [161] bed = bid, offered. [162] new fangle = capricious. [163] gaule = gules, red. [164] wadded = of woad colour, blue. [165] blee = hue. [166] can = did. [167] walker = fuller. [168] rudd = complexion. [169] For why = because. [170] dinne = noise, i. e. ado. [171] crowt = pucker. [172] shreeven = shriven, confessed. [173] werryed = worried. [174] birtled = brittled, cut up. [175] ronge = rung, resounded. [176] sheede = shed, spill.
- 72. 18. King Arthur and King Cornwall A Fragment King Arthur of Little Britain unwisely boasts the beauty of his famous Round Table.
- 73. I Saies, ‘Come here, cuzen Gawaine so gay, My sisters sonne be yee; Ffor you shall see one of the fairest round tables That ever you see with your eye.’ II Then bespake Lady Queen Guenever, And these were the words said shee: ‘I know where a round table is, thou noble king, Is worth thy round table and other such three. III ‘The trestle that stands under this round table,’ she said, ‘Lowe downe to the mould, It is worth thy round table, thou worthy king, Thy halls, and all thy gold. IV ‘The place where this round table stands in, [Is fencèd round amaine] It is worth thy castle, thy gold, thy fee, And all good Litle Britaine.’ V ‘Where may that table be, lady?’ quoth hee, ‘Or where may all that goodly building be?’ ‘You shall it seeke,’ shee says, ‘till you it find;
- 74. You shall it seeke, shee says, till you it find; You shall never gett more of me.’ VI Then bespake him noble King Arthur These were the words said hee: ‘I’le make mine avow to God, And alsoe to the Trinity, VII ‘I’le never sleepe one night there as I doe another ’Till that round table I see: Sir Marramiles and Sir Tristeram, Fellowes that ye shall bee. VIII [‘Sir Gawaine and Sir Bredbettle Be fellowes eke with me,] Weele be clad in palmers’ weede, Five palmers we will bee; IX ‘There is noe outlandish man will us abide, Nor will us come nye.’ Then they rived[177] east and they rived west, In many a strange countrỳ. X Then they tranckled[178] a litle further,
- 75. They saw a battle new sett: ‘Now, by my faith,’ saies noble King Arthur, [‘These armies be well met.’] After travelling in many strange lands they arrive at the castle of King Cornwall, not a great way from home.
- 76. XI But when he cam to this [Cornwall castle] And to the palace gate, Soe ready was ther a proud portèr, And met him soone therat. XII Shooes of gold the porter had on, And all his other rayment was unto the same: ‘Now, by my faith,’ saies noble King Arthur, ‘Yonder is a minion swaine.’ XIII Then bespake noble King Arthur, These were the words says hee: ‘Come thou hither, thou proud portèr, I pray thee come hither to me. XIV ‘I have two poore rings, of my finger, The better of them I’le give to thee; Tell who may be lord of this castle, Or who is lord in this cuntry?’ XV ‘Cornewall King,’ the porter sayes, ‘There is none soe rich as hee; Neither in christendome, nor yet in heathendom,
- 77. Neither in christendome, nor yet in heathendom, None hath soe much gold as he.’ XVI And then bespake him noble King Arthur, These were the words sayes hee: ‘I have two poore rings of my finger, The better of them I’le give thee, If thou wilt greete him well, Cornewall King, And greete him well from me. XVII ‘Pray him for one night’s lodging and two meales’ meate, For his love that dyed uppon a tree; Of one ghesting[179] and two meales’ meate, For his love that dyed uppon tree. XVIII ‘Of one ghesting, of two meales’ meate, For his love that was of virgin borne, And in the morning that we may scape away, Either without scath or scorne.’ XIX Then forth is gone this proud portèr, As fast as he co’ld hye, And when he came befor Cornewall King, He kneelèd downe on his knee. XX
- 78. Sayes, ‘I have beene porter-man at thy gate This thirty winter and three, [But there is ffive knights before itt now, The like I never did see.’] King Cornwall questioning the strangers, they happen to speak of a certain shrine of Our Lady, from which he gathers that they have been in Little Britain. This leads him to question them concerning King Arthur.
- 79. XXI Our Lady was borne; then thought Cornewall King ‘These palmers had beene in Brittaine.’ XXII Then bespake him Cornewall King, These were the words he said there: ‘Did you ever know a comely king, His name was King Arthùr?’ XXIII And then bespake him noble King Arthùr, These were the words said hee: ‘I doe not know that comly king, But once my selfe I did him see.’ Then bespake Cornewall King againe, These were the words said he: XXIV Sayes, ‘Seven yeere I was clad and fed, In Litle Brittaine, in a bower; I had a daughter by King Arthur’s wife, That now is called my flower; For King Arthur, that kindly cockward, Hath none such in his bower. XXV
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