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Cyber-Physical Systems

Foundations and Techniques
Edited by Uzzal Sharma, Parma Nand, Jyotir Moy Chatterjee, Vishal Jain, Noor Zaman Jhanjhi and R. Sujatha
Copyright: 2022   |   Status: Published
ISBN: 9781119836193  |  Hardcover  |  
334 pages | 92 illustrations
Price: $195 USD
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One Line Description
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.

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

Description
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.


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Author / Editor Details
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, Taylor’s University, Malaysia.

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Table of Contents
Preface
Acknowledgement
1. A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things
Ravi Gedam and Surendra Rahamatkar
1.1 Introduction
1.2 Background of Industrial Internet of Things
1.3 Literature Review
1.4 The Proposed Methodology
1.5 Experimental Requirements
1.6 Conclusion
References
2. Integration of Big Data Analytics Into Cyber-Physical Systems
Nandhini R.S. and Ramanathan L.
2.1 Introduction
2.2 Big Data Model for Cyber-Physical System
2.2.1 Cyber-Physical System Architecture
2.2.2 Big Data Analytics Model
2.3 Big Data and Cyber-Physical System Integration
2.3.1 Big Data Analytics and Cyber-Physical System
2.3.1.1 Integration of CPS With BDA
2.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics
2.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System
2.4 Storage and Communication of Big Data for Cyber-Physical System
2.4.1 Big Data Storage for Cyber-Physical System
2.4.2 Big Data Communication for Cyber-Physical System
2.5 Big Data Processing in Cyber-Physical System
2.5.1 Data Processing
2.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing
2.5.1.2 Clustering in Big Data
2.5.1.3 Clustering in Cyber-Physical System
2.5.2 Big Data Analytics
2.6 Applications of Big Data for Cyber-Physical System
2.6.1 Manufacturing
2.6.2 Smart Grids and Smart Cities
2.6.3 Healthcare
2.6.4 Smart Transportation
2.7 Security and Privacy
2.8 Conclusion
References
3. Machine Learning: A Key Towards Smart Cyber-Physical Systems Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur
3.1 Introduction
3.2 Different Machine Learning Algorithms
3.2.1 Performance Measures for Machine Learning Algorithms
3.2.2 Steps to Implement ML Algorithms
3.2.3 Various Platforms Available for Implementation
3.2.4 Applications of Machine Learning in Electrical Engineering
3.3 ML Use-Case in MATLAB
3.4 ML Use-Case in Python
3.4.1 ML Model Deployment
3.5 Conclusion
References
4. Precise Risk Assessment and Management
Ambika N.
4.1 Introduction
4.2 Need for Security
4.2.1 Confidentiality
4.2.2 Integrity
4.2.3 Availability
4.2.4 Accountability
4.2.5 Auditing
4.3 Different Kinds of Attacks
4.3.1 Malware
4.3.2 Man-in-the Middle Assault
4.3.3 Brute Force Assault
4.3.4 Distributed Denial of Service
4.4 Literature Survey
4.5 Proposed Work
4.5.1 Objective
4.5.2 Notations Used in the Contribution
4.5.3 Methodology
4.5.4 Simulation and Analysis
4.6 Conclusion
References
5. A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment
Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja
5.1 Introduction
5.2 IoT Components, Layered Architectures, Security Threats
5.2.1 IoT Components
5.2.2 IoT Layered Architectures
5.2.2.1 3-Layer Architecture
5.2.2.2 4-Layer Architecture
5.2.2.3 5-Layer Architecture
5.2.3 Associated Threats in the Layers
5.2.3.1 Node Capture
5.2.3.2 Playback Attack
5.2.3.3 Fake Node Augmentation
5.2.3.4 Timing Attack
5.2.3.5 Bootstrap Attack
5.2.3.6 Jamming Attack
5.2.3.7 Kill Command Attack
5.2.3.8 Denial-of-Service (DoS) Attack
5.2.3.9 Storage Attack
5.2.3.10 Exploit Attack
5.2.3.11 Man-In-The-Middle (MITM) Attack
5.2.3.12 XSS Attack
5.2.3.13 Malicious Insider Attack
5.2.3.14 Malwares
5.2.3.15 Zero-Day Attack
5.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT
5.3.1 Taxonomy of DDoS Attacks
5.3.1.1 Architectural Model
5.3.1.2 Exploited Vulnerability
5.3.1.3 Protocol Level
5.3.1.4 Degree of Automation
5.3.1.5 Scanning Techniques
5.3.1.6 Propagation Mechanism
5.3.1.7 Impact Over the Victim
5.3.1.8 Rate of Attack
5.3.1.9 Persistence of Agents
5.3.1.10 Validity of Source Address
5.3.1.11 Type of Victim
5.3.1.12 Attack Traffic Distribution
5.3.2 Working Mechanism of DDoS Attack
5.4 Existing Solution Mechanisms Against DDoS Over IoT
5.4.1 Detection Techniques
5.4.2 Prevention Mechanisms
5.5 Challenges and Research Directions
5.6 Conclusion
References
6. Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges
Bhimavarapu Usharani
6.1 Introduction
6.2 Phishing Threats
6.2.1 Internet Fraud
6.2.1.1 Electronic-Mail Fraud
6.2.1.2 Phishing Extortion
6.2.1.3 Extortion Fraud
6.2.1.4 Social Media Fraud
6.2.1.5 Tourism Fraud
6.2.1.6 Excise Fraud
6.2.2 Phishing
6.3 Deep Learning Architectures
6.3.1 Convolution Neural Network (CNN) Models
6.3.1.1 Recurrent Neural Network
6.3.1.2 Long Short-Term Memory (LSTM)
6.4 Related Work
6.4.1 Machine Learning Approach
6.4.2 Neural Network Approach
6.4.3 Deep Learning Approach
6.5 Analysis Report
6.6 Current Challenges
6.6.1 File-Less Malware
6.6.2 Crypto Mining
6.7 Conclusions
References
7. Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks
Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar
7.1 Introduction
7.2 Literature Review
7.3 Classification of Attacks
7.3.1 The Perception Layer Network Attacks
7.3.2 Network Attacks on the Application Control Layer
7.3.3 Data Transmission Layer Network Attacks
7.3.3.1 Rogue Access Point
7.3.3.2 ARP Spoofing
7.3.3.3 DNS Spoofing
7.3.3.4 mDNS Spoofing
7.3.3.5 SSL Stripping
7.4 Proposed Algorithm of Detection and Prevention
7.4.1 ARP Spoofing
7.4.2 Rogue Access Point and SSL Stripping
7.4.3 DNS Spoofing
7.5 Results and Discussion
7.6 Conclusion and Future Scope
References
8. Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications
Saurav S. and Arnab Ghosh
8.1 Introduction
8.2 Modeling of Fourth Order Interleaved Boost Converter
8.2.1 Introduction to the Topology
8.2.2 Modeling of FIBC
8.2.2.1 Mode 1 Operation (0 to d1Ts)
8.2.2.2 Mode 2 Operation (d1Ts to d2Ts)
8.2.2.3 Mode 3 Operation (d2Ts to d3Ts)
8.2.2.4 Mode 4 Operation (d3Ts to Ts)
8.2.3 Averaging of the Model
8.2.4 Small Signal Analysis
8.3 Controller Design for FIBC
8.3.1 PID Controller
8.3.2 Type II Controller
8.3.3 Type III Controller
8.4 Computational Results
8.5 Conclusion
References
9. Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management
Somya Goyal
9.1 Introduction
9.1.1 RFID and IoT for Smart Inventory Management
9.2 Benefits and Barriers in Implementation of RFID
9.2.1 Benefits
9.2.1.1 Routine Automation
9.2.1.2 Improvement in the Visibility of Assets and Quick Availability
9.2.1.3 SCM-Business Benefits
9.2.1.4 Automated Lost and Found
9.2.1.5 Smart Investment on Inventory
9.2.1.6 Automated Patient Tracking
9.2.2. Barriers
9.2.2.1 RFID May Interfere With Medical Activities
9.2.2.2 Extra Maintenance for RFID Tags
9.2.2.3 Expense Overhead
9.2.2.4 Interoperability Issues
9.2.2.5 Security Issues
9.3 IoT-Based Inventory Management—Case Studies
9.4 Proposed Model for RFID-Based Hospital Management
9.5 Conclusion and Future Scope
References
10. A Systematic Study of Security of Industrial IoT
Ravi Gedam and Surendra Rahamatkar
10.1 Introduction
10.2 Overview of Industrial Internet of Things (Smart Manufacturing)
10.2.1 Key Enablers in Industry 4.0
10.2.2 OPC Unified Architecture (OPC UA)
10.3 Industrial Reference Architecture
10.3.1 Arrowgead
10.3.2 FIWARE
10.3.3 Industrial Internet Reference Architecture (IIRA)
10.3.4 Kaa IoT Platform
10.3.5 Open Connectivity Foundation (OCF)
10.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0)
10.3.7 ThingsBoard
10.3.8 ThingSpeak
10.3.9 ThingWorx
10.4 FIWARE Generic Enabler (FIWARE GE)
10.4.1 Core Context Management GE
10.4.2 NGSI Context Data Model
10.4.3 IDAS IoT Agents
10.4.3.1 IoT Agent-JSON
10.4.3.2 IoT Agent-OPC UA
10.4.3.2 Context Provider
10.4.4 FIWARE for Smart Industry
10.5 Discussion
10.5.1 Solutions Adopting FIWARE
10.5.2 IoT Interoperability Testing
10.6 Conclusion
References
11. Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems
Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee
11.1 Introduction
11.2 Popular Privacy Design Recommendations
11.2.1 Dynamic Authorization
11.2.2 End to End Security
11.2.3 Enrollment and Authentication APIs
11.2.4 Distributed Authorization
11.2.5 Decentralization Authentication
11.2.6 Interoperable Privacy Profiles
11.3 Current Privacy Challenges in CPS
11.4 Privacy Aware Design for CPS
11.5 Limitations
11.6 Converting Risks of Applying AI Into Advantages
11.6.1 Proof of Recognition and De-Anonymization
11.6.2 Segregation, Shamefulness, Mistakes
11.6.3 Haziness and Bias of Profiling
11.6.4 Abuse Arising From Information
11.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem
11.7 Conclusion and Future Scope
References
12. Exposing Security and Privacy Issues on Cyber-Physical Systems
Keshav Kaushik
12.1 Introduction to Cyber-Physical Systems (CPS)
12.2 Cyber-Attacks and Security in CPS
12.3 Privacy in CPS
12.4 Conclusion & Future Trends in CPS Security
References
13. Applications of Cyber-Physical Systems
Amandeep Kaur and Jyotir Moy Chatterjee
13.1 Introduction
13.2 Applications of Cyber-Physical Systems
13.2.1 Healthcare
13.2.1.1 Related Work
13.2.2 Education
13.2.2.1 Related Works
13.2.3 Agriculture
13.2.3.1 Related Work
13.2.4 Energy Management
13.2.4.1 Related Work
13.2.5 Smart Transportation
13.2.5.1 Related Work
13.2.6 Smart Manufacturing
13.2.6.1 Related Work
13.2.7 Smart Buildings: Smart Cities and Smart Houses
13.2.7.1 Related Work
13.3 Conclusion
References
Index

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