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E-raamat: Cyber-Physical Systems: Foundations and Techniques

Edited by (School of Technology, Assam Don Bosco University, Guwahati, India), Edited by (Taylor's Uni), Edited by (Sharda University, Greater Noida, U. P. India), Edited by , Edited by (Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal), Edited by (Indian Institute of Technology, Roorkee)
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  • Ilmumisaeg: 07-Jun-2022
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119836629
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Cyber-Physical Systems: Foundations and TechniquesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Jun-2022
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119836629
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CYBER-PHYSICAL SYSTEMS

The 13 chapters in this book cover the various aspects associated with Cyber-Physical Systems (CPS) such as algorithms, application areas, and the improvement of existing technology such as machine learning, big data and robotics.

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 Systems.” 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 individual contribution of each process from the output. Some exciting innovations such as autonomous cars, quadcopter, spaceships, sophisticated medical devices fall under CPS.

The scope of CPS is tremendous. In CPS, one sees the applications of various emerging technologies such as artificial intelligence (AI), Internet of Things (IoT), machine learning (ML), deep learning (DL), big data (BD), robotics, quantum technology, etc. In almost all sectors, whether it is education, health, human resource development, skill improvement, startup strategy, etc., one sees an enhancement in the quality of output because of the emergence of CPS into the field.

Audience
Researchers in Information technology, artificial intelligence, robotics, electronics and electrical engineering.

Preface xv
Acknowledgement xix
1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things
1(18)
Ravi Gedam
Surendra Rahamatkar
1.1 Introduction
2(1)
1.2 Background of Industrial Internet of Things
3(3)
1.3 Literature Review
6(7)
1.4 The Proposed Methodology
13(1)
1.5 Experimental Requirements
14(1)
1.6 Conclusion
15(4)
References
16(3)
2 Integration of Big Data Analytics Into Cyber-Physical Systems
19(24)
Nandhini R.S.
L. Ramanathan
2.1 Introduction
19(2)
2.2 Big Data Model for Cyber-Physical System
21(2)
2.2.1 Cyber-Physical System Architecture
22(1)
2.2.2 Big Data Analytics Model
22(1)
2.3 Big Data and Cyber-Physical System Integration
23(3)
2.3.1 Big Data Analytics and Cyber-Physical System
23(1)
2.3.1.1 Integration of CPS With BDA
24(1)
2.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics
24(1)
2.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System
25(1)
2.4 Storage and Communication of Big Data for Cyber-Physical System
26(3)
2.4.1 Big Data Storage for Cyber-Physical System
27(1)
2.4.2 Big Data Communication for Cyber-Physical System
28(1)
2.5 Big Data Processing in Cyber-Physical System
29(4)
2.5.1 Data Processing
29(1)
2.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing
29(2)
2.5.1.2 Clustering in Big Data
31(1)
2.5.1.3 Clustering in Cyber-Physical System
32(1)
2.5.2 Big Data Analytics
32(1)
2.6 Applications of Big Data for Cyber-Physical System
33(3)
2.6.1 Manufacturing
33(1)
2.6.2 Smart Grids and Smart Cities
34(1)
2.6.3 Healthcare
35(1)
2.6.4 Smart Transportation
35(1)
2.7 Security and Privacy
36(1)
2.8 Conclusion
37(6)
References
38(5)
3 Machine Learning: A Key Towards Smart Cyber-Physical Systems
43(20)
Rashmi Kapoor
Chandragiri Radhacharan
Sung-ho Hur
3.1 Introduction
44(2)
3.2 Different Machine Learning Algorithms
46(5)
3.2.1 Performance Measures for Machine Learning Algorithms
48(1)
3.2.2 Steps to Implement ML Algorithms
49(1)
3.2.3 Various Platforms Available for Implementation
50(1)
3.2.4 Applications of Machine Learning in Electrical Engineering
50(1)
3.3 ML Use-Case in MATLAB
51(5)
3.4 ML Use-Case in Python
56(4)
3.4.1 ML Model Deployment
59(1)
3.5 Conclusion
60(3)
References
60(3)
4 Precise Risk Assessment and Management
63(22)
N. Ambika
4.1 Introduction
64(1)
4.2 Need for Security
65(2)
4.2.1 Confidentiality
65(1)
4.2.2 Integrity
66(1)
4.2.3 Availability
66(1)
4.2.4 Accountability
66(1)
4.2.5 Auditing
67(1)
4.3 Different Kinds of Attacks
67(3)
4.3.1 Malware
67(2)
4.3.2 Man-in-the Middle Assault
69(1)
4.3.3 Brute Force Assault
69(1)
4.3.4 Distributed Denial of Service
69(1)
4.4 Literature Survey
70(5)
4.5 Proposed Work
75(5)
4.5.1 Objective
75(1)
4.5.2 Notations Used in the Contribution
76(1)
4.5.3 Methodology
76(2)
4.5.4 Simulation and Analysis
78(2)
4.6 Conclusion
80(5)
References
80(5)
5 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment
85(38)
Rajarajan Ganesarathinam
Muthukumaran Singaravelu
K.N. Padma Pooja
5.1 Introduction
86(3)
5.2 IoT Components, Layered Architectures, Security Threats
89(8)
5.2.1 IoT Components
89(1)
5.2.2 IoT Layered Architectures
90(1)
5.2.2.1 3-Layer Architecture
91(1)
5.2.2.2 4-Layer Architecture
91(2)
5.2.2.3 5-Layer Architecture
93(1)
5.2.3 Associated Threats in the Layers
93(1)
5.2.3.1 Node Capture
93(1)
5.2.3.2 Playback Attack
93(1)
5.2.3.3 Fake Node Augmentation
93(1)
5.2.3.4 Timing Attack
94(1)
5.2.3.5 Bootstrap Attack
94(1)
5.2.3.6 Jamming Attack
94(1)
5.2.3.7 Kill Command Attack
94(1)
5.2.3.8 Denial-of-Service (DoS) Attack
94(1)
5.2.3.9 Storage Attack
94(1)
5.2.3.10 Exploit Attack
95(1)
5.2.3.11 Man-In-The-Middle (MITM) Attack
95(1)
5.2.3.12 XSS Attack
95(1)
5.2.3.13 Malicious Insider Attack
95(1)
5.2.3.14 Malwares
95(1)
5.2.3.15 Zero-Day Attack
95(2)
5.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT
97(8)
5.3.1 Taxonomy of DDoS Attacks
99(1)
5.3.1.1 Architectural Model
99(1)
5.3.1.2 Exploited Vulnerability
100(1)
5.3.1.3 Protocol Level
101(1)
5.3.1.4 Degree of Automation
101(1)
5.3.1.5 Scanning Techniques
101(1)
5.3.1.6 Propagation Mechanism
102(1)
5.3.1.7 Impact Over the Victim
102(1)
5.3.1.8 Rate of Attack
103(1)
5.3.1.9 Persistence of Agents
103(1)
5.3.1.10 Validity of Source Address
103(1)
5.3.1.11 Type of Victim
103(1)
5.3.1.12 Attack Traffic Distribution
103(1)
5.3.2 Working Mechanism of DDoS Attack
104(1)
5.4 Existing Solution Mechanisms Against DDoS Over IoT
105(8)
5.4.1 Detection Techniques
105(3)
5.4.2 Prevention Mechanisms
108(5)
5.5 Challenges and Research Directions
113(2)
5.6 Conclusion
115(8)
References
115(8)
6 Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges
123(24)
Bhitnavarapu Usharani
6.1 Introduction
124(1)
6.2 Phishing Threats
124(7)
6.2.1 Internet Fraud
124(1)
6.2.1.1 Electronic-Mail Fraud
125(1)
6.2.1.2 Phishing Extortion
126(1)
6.2.1.3 Extortion Fraud
127(1)
6.2.1.4 Social Media Fraud
127(1)
6.2.1.5 Tourism Fraud
128(1)
6.2.1.6 Excise Fraud
129(1)
6.2.2 Phishing
129(2)
6.3 Deep Learning Architectures
131(4)
6.3.1 Convolution Neural Network (CNN) Models
131(1)
6.3.1.1 Recurrent Neural Network
131(3)
6.3.1.2 Long Short-Term Memory (LSTM)
134(1)
6.4 Related Work
135(4)
6.4.1 Machine Learning Approach
135(1)
6.4.2 Neural Network Approach
136(2)
6.4.3 Deep Learning Approach
138(1)
6.5 Analysis Report
139(1)
6.6 Current Challenges
140(1)
6.6.1 File-Less Malware
140(1)
6.6.2 Crypto Mining
140(1)
6.7 Conclusions
140(7)
References
141(6)
7 Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks
147(32)
Gaurav Narula
Preeti Nagrath
Drishti Hans
Anand Nayyar
7.1 Introduction
148(2)
7.2 Literature Review
150(2)
7.3 Classification of Attacks
152(10)
7.3.1 The Perception Layer Network Attacks
152(1)
7.3.2 Network Attacks on the Application Control Layer
153(1)
7.3.3 Data Transmission Layer Network Attacks
153(1)
7.3.3.1 Rogue Access Point
154(1)
7.3.3.2 ARP Spoofing
155(2)
7.3.3.3 DNS Spoofing
157(3)
7.3.3.4 MDNS Spoofing
160(1)
7.3.3.5 SSL Stripping
161(1)
7.4 Proposed Algorithm of Detection and Prevention
162(11)
7.4.1 ARP Spoofing
162(6)
7.4.2 Rogue Access Point and SSL Stripping
168(1)
7.4.3 DNS Spoofing
169(4)
7.5 Results and Discussion
173(1)
7.6 Conclusion and Future Scope
173(6)
References
174(5)
8 Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications
179(30)
S. Saurav
Arnab Ghosh
8.1 Introduction
179(2)
8.2 Modeling of Fourth Order Interleaved Boost Converter
181(12)
8.2.1 Introduction to the Topology
181(1)
8.2.2 Modeling of FIBC
182(1)
8.2.2.1 Mode 1 Operation (0 to d1 Ts)
182(2)
8.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts)
184(2)
8.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts)
186(2)
8.2.2.4 Mode 4 Operation (d3 Ts to Ts)
188(2)
8.2.3 Averaging of the Model
190(1)
8.2.4 Small Signal Analysis
190(3)
8.3 Controller Design for FIBC
193(4)
8.3.1 PID Controller
193(1)
8.3.2 Type II Controller
194(1)
8.3.3 Type III Controller
195(2)
8.4 Computational Results
197(7)
8.5 Conclusion
204(5)
References
205(4)
9 Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management
209(20)
Somya Goyal
9.1 Introduction
210(2)
9.1.1 RFID and IoT for Smart Inventory Management
210(2)
9.2 Benefits and Barriers in Implementation of RFID
212(6)
9.2.1 Benefits
213(1)
9.2.1.1 Routine Automation
213(2)
9.2.1.2 Improvement in the Visibility of Assets and Quick Availability
215(1)
9.2.1.3 SCM-Business Benefits
215(1)
9.2.1.4 Automated Lost and Found
216(1)
9.2.1.5 Smart Investment on Inventory
217(1)
9.2.1.6 Automated Patient Tracking
217(1)
9.2.2 Barriers
218(1)
9.2.2.1 RFID May Interfere With Medical Activities
218(1)
9.2.2.2 Extra Maintenance for RFID Tags
218(1)
9.2.2.3 Expense Overhead
218(1)
9.2.2.4 Interoperability Issues
218(1)
9.2.2.5 Security Issues
218(1)
9.3 IoT-Based Inventory Management--Case Studies
218(2)
9.4 Proposed Model for RFID-Based Hospital Management
220(5)
9.5 Conclusion and Future Scope
225(4)
References
226(3)
10 A Systematic Study of Security of Industrial IoT
229(28)
Ravi Gedam
Surendra Rahamatkar
10.1 Introduction
230(1)
10.2 Overview of Industrial Internet of Things (Smart Manufacturing)
231(5)
10.2.1 Key Enablers in Industry 4.0
233(1)
10.2.2 OPC Unified Architecture (OPC UA)
234(2)
10.3 Industrial Reference Architecture
236(5)
10.3.1 Arrowgead
237(1)
10.3.2 FIWARE
237(1)
10.3.3 Industrial Internet Reference Architecture (IIRA)
238(1)
10.3.4 Kaa IoT Platform
238(1)
10.3.5 Open Connectivity Foundation (OCF)
239(1)
10.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0)
239(1)
10.3.7 ThingsBoard
240(1)
10.3.8 ThingSpeak
240(1)
10.3.9 ThingWorx
240(1)
10.4 FIWARE Generic Enabler (FIWARE GE)
241(8)
10.4.1 Core Context Management GE
241(1)
10.4.2 NGSI Context Data Model
242(2)
10.4.3 IDAS IoT Agents
244(2)
10.4.3.1 IoT Agent-JSON
246(1)
10.4.3.2 IoT Agent-OPC UA
247(1)
10.4.3.3 Context Provider
247(1)
10.4.4 FIWARE for Smart Industry
248(1)
10.5 Discussion
249(3)
10.5.1 Solutions Adopting FIWARE
250(1)
10.5.2 IoT Interoperability Testing
251(1)
10.6 Conclusion
252(5)
References
253(4)
11 Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems
257(16)
Manas Kumar Yogi
A.S.N. Chakravarthy
Jyotir Moy Chatterjee
11.1 Introduction
258(1)
11.2 Popular Privacy Design Recommendations
258(4)
11.2.1 Dynamic Authorization
258(1)
11.2.2 End to End Security
259(1)
11.2.3 Enrollment and Authentication APIs
259(1)
11.2.4 Distributed Authorization
260(1)
11.2.5 Decentralization Authentication
261(1)
11.2.6 Interoperable Privacy Profiles
261(1)
11.3 Current Privacy Challenges in CPS
262(1)
11.4 Privacy Aware Design for CPS
263(2)
11.5 Limitations
265(1)
11.6 Converting Risks of Applying AI Into Advantages
266(3)
11.6.1 Proof of Recognition and De-Anonymization
267(1)
11.6.2 Segregation, Shamefulness, Mistakes
267(1)
11.6.3 Haziness and Bias of Profiling
267(1)
11.6.4 Abuse Arising From Information
267(1)
11.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem
268(1)
11.7 Conclusion and Future Scope
269(4)
References
270(3)
12 Exposing Security and Privacy Issues on Cyber-Physical Systems
273(16)
Keshav Kaushik
12.1 Introduction to Cyber-Physical Systems (CPS)
273(4)
12.2 Cyber-Attacks and Security in CPS
277(4)
12.3 Privacy in CPS
281(3)
12.4 Conclusion & Future Trends in CPS Security
284(5)
References
285(4)
13 Applications of Cyber-Physical Systems
289(16)
Amandeep Kaur
Jyotir Moy Chatterjee
13.1 Introduction
289(2)
13.2 Applications of Cyber-Physical Systems
291(13)
13.2.1 Healthcare
291(2)
13.2.1.1 Related Work
293(2)
13.2.2 Education
295(1)
13.2.2.1 Related Works
295(1)
13.2.3 Agriculture
296(1)
13.2.3.1 Related Work
297(1)
13.2.4 Energy Management
298(1)
13.2.4.1 Related Work
299(1)
13.2.5 Smart Transportation
300(1)
13.2.5.1 Related Work
301(1)
13.2.6 Smart Manufacturing
301(2)
13.2.6.1 Related Work
303(1)
13.2.7 Smart Buildings: Smart Cities and Smart Houses
303(1)
13.2.7.1 Related Work
304(1)
13.3 Conclusion
304(1)
References 305(6)
Index 311
Uzzal Sharma, PhD, is an assistant professor (senior), Department of Computer Applications, School of Technology, Assam Don Bosco University, Guwahati, India.

Parma Nand, PhD, in Computer Science & Engineering from Indian Institute of Technology, Roorkee, and has more than 27 years of experience, both in industry and academia.

Jyotir Moy Chatterjee is an assistant professor in the Information Technology department at Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal.

Vishal Jain, PhD, is an associate professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P. India.

Noor Zaman Jhanjhi, PhD, is an associate professor, Director of the Center for Smart Society 5.0 at the School of Computer Science and Engineering, Faculty of Innovation and Technology, Taylors University, Malaysia.

R. Sujatha, PhD, is an associate professor in the School of Information Technology and Engineering in Vellore Institute of Technology, Vellore, India.
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