Cyber-Physical Systems: Foundations and Techniques

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
Acknowledgement
1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
			10.4.3.3 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
	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
	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
	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
EULA