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

• Preface xvAcknowledgement xix1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things 1Ravi Gedam and Surendra Rahamatkar1.1 Introduction 21.2 Background of Industrial Internet of Things 31.3 Literature Review 61.4 The Proposed Methodology 131.5 Experimental Requirements 141.6 Conclusion 15References 162 Integration of Big Data Analytics Into Cyber-Physical Systems 19Nandhini R.S. and Ramanathan L.2.1 Introduction 192.2 Big Data Model for Cyber-Physical System 212.2.1 Cyber-Physical System Architecture 222.2.2 Big Data Analytics Model 222.3 Big Data and Cyber-Physical System Integration 232.3.1 Big Data Analytics and Cyber-Physical System 232.3.1.1 Integration of CPS With BDA 242.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics 242.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System 252.4 Storage and Communication of Big Data for Cyber-Physical System 262.4.1 Big Data Storage for Cyber-Physical System 272.4.2 Big Data Communication for Cyber-Physical System 282.5 Big Data Processing in Cyber-Physical System 292.5.1 Data Processing 292.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing 292.5.1.2 Clustering in Big Data 312.5.1.3 Clustering in Cyber-Physical System 322.5.2 Big Data Analytics 322.6 Applications of Big Data for Cyber-Physical System 332.6.1 Manufacturing 332.6.2 Smart Grids and Smart Cities 342.6.3 Healthcare 352.6.4 Smart Transportation 352.7 Security and Privacy 362.8 Conclusion 37References 383 Machine Learning: A Key Towards Smart Cyber-Physical Systems 43Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur3.1 Introduction 443.2 Different Machine Learning Algorithms 463.2.1 Performance Measures for Machine Learning Algorithms 483.2.2 Steps to Implement ML Algorithms 493.2.3 Various Platforms Available for Implementation 503.2.4 Applications of Machine Learning in Electrical Engineering 503.3 ML Use-Case in MATLAB 513.4 ML Use-Case in Python 563.4.1 ML Model Deployment 593.5 Conclusion 60References 604 Precise Risk Assessment and Management 63Ambika N.4.1 Introduction 644.2 Need for Security 654.2.1 Confidentiality 654.2.2 Integrity 664.2.3 Availability 664.2.4 Accountability 664.2.5 Auditing 674.3 Different Kinds of Attacks 674.3.1 Malware 674.3.2 Man-in-the Middle Assault 694.3.3 Brute Force Assault 694.3.4 Distributed Denial of Service 694.4 Literature Survey 704.5 Proposed Work 754.5.1 Objective 754.5.2 Notations Used in the Contribution 764.5.3 Methodology 764.5.4 Simulation and Analysis 784.6 Conclusion 80References 805 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment 85Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja5.1 Introduction 865.2 IoT Components, Layered Architectures, Security Threats 895.2.1 IoT Components 895.2.2 IoT Layered Architectures 905.2.2.1 3-Layer Architecture 915.2.2.2 4-Layer Architecture 915.2.2.3 5-Layer Architecture 935.2.3 Associated Threats in the Layers 935.2.3.1 Node Capture 935.2.3.2 Playback Attack 935.2.3.3 Fake Node Augmentation 935.2.3.4 Timing Attack 945.2.3.5 Bootstrap Attack 945.2.3.6 Jamming Attack 945.2.3.7 Kill Command Attack 945.2.3.8 Denial-of-Service (DoS) Attack 945.2.3.9 Storage Attack 945.2.3.10 Exploit Attack 955.2.3.11 Man-In-The-Middle (MITM) Attack 955.2.3.12 XSS Attack 955.2.3.13 Malicious Insider Attack 955.2.3.14 Malwares 955.2.3.15 Zero-Day Attack 955.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT 975.3.1 Taxonomy of DDoS Attacks 995.3.1.1 Architectural Model 995.3.1.2 Exploited Vulnerability 1005.3.1.3 Protocol Level 1015.3.1.4 Degree of Automation 1015.3.1.5 Scanning Techniques 1015.3.1.6 Propagation Mechanism 1025.3.1.7 Impact Over the Victim 1025.3.1.8 Rate of Attack 1035.3.1.9 Persistence of Agents 1035.3.1.10 Validity of Source Address 1035.3.1.11 Type of Victim 1035.3.1.12 Attack Traffic Distribution 1035.3.2 Working Mechanism of DDoS Attack 1045.4 Existing Solution Mechanisms Against DDoS Over IoT 1055.4.1 Detection Techniques 1055.4.2 Prevention Mechanisms 1085.5 Challenges and Research Directions 1135.6 Conclusion 115References 1156 Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges 123Bhimavarapu Usharani6.1 Introduction 1246.2 Phishing Threats 1246.2.1 Internet Fraud 1246.2.1.1 Electronic-Mail Fraud 1256.2.1.2 Phishing Extortion 1266.2.1.3 Extortion Fraud 1276.2.1.4 Social Media Fraud 1276.2.1.5 Tourism Fraud 1286.2.1.6 Excise Fraud 1296.2.2 Phishing 1296.3 Deep Learning Architectures 1316.3.1 Convolution Neural Network (CNN) Models 1316.3.1.1 Recurrent Neural Network 1316.3.1.2 Long Short-Term Memory (LSTM) 1346.4 Related Work 1356.4.1 Machine Learning Approach 1356.4.2 Neural Network Approach 1366.4.3 Deep Learning Approach 1386.5 Analysis Report 1396.6 Current Challenges 1406.6.1 File-Less Malware 1406.6.2 Crypto Mining 1406.7 Conclusions 140References 1417 Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks 147Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar7.1 Introduction 1487.2 Literature Review 1507.3 Classification of Attacks 1527.3.1 The Perception Layer Network Attacks 1527.3.2 Network Attacks on the Application Control Layer 1537.3.3 Data Transmission Layer Network Attacks 1537.3.3.1 Rogue Access Point 1547.3.3.2 ARP Spoofing 1557.3.3.3 DNS Spoofing 1577.3.3.4 mDNS Spoofing 1607.3.3.5 SSL Stripping 1617.4 Proposed Algorithm of Detection and Prevention 1627.4.1 ARP Spoofing 1627.4.2 Rogue Access Point and SSL Stripping 1687.4.3 DNS Spoofing 1697.5 Results and Discussion 1737.6 Conclusion and Future Scope 173References 1748 Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications 179Saurav S. and Arnab Ghosh8.1 Introduction 1798.2 Modeling of Fourth Order Interleaved Boost Converter 1818.2.1 Introduction to the Topology 1818.2.2 Modeling of FIBC 1828.2.2.1 Mode 1 Operation (0 to d1 Ts) 1828.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts) 1848.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts) 1868.2.2.4 Mode 4 Operation (d3 Ts to Ts) 1888.2.3 Averaging of the Model 1908.2.4 Small Signal Analysis 1908.3 Controller Design for FIBC 1938.3.1 PID Controller 1938.3.2 Type II Controller 1948.3.3 Type III Controller 1958.4 Computational Results 1978.5 Conclusion 204References 2059 Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management 209Somya Goyal9.1 Introduction 2109.1.1 RFID and IoT for Smart Inventory Management 2109.2 Benefits and Barriers in Implementation of RFID 2129.2.1 Benefits 2139.2.1.1 Routine Automation 2139.2.1.2 Improvement in the Visibility of Assets and Quick Availability 2159.2.1.3 SCM-Business Benefits 2159.2.1.4 Automated Lost and Found 2169.2.1.5 Smart Investment on Inventory 2179.2.1.6 Automated Patient Tracking 2179.2.2 Barriers 2189.2.2.1 RFID May Interfere With Medical Activities 2189.2.2.2 Extra Maintenance for RFID Tags 2189.2.2.3 Expense Overhead 2189.2.2.4 Interoperability Issues 2189.2.2.5 Security Issues 2189.3 IoT-Based Inventory Management—Case Studies 2189.4 Proposed Model for RFID-Based Hospital Management 2209.5 Conclusion and Future Scope 225References 22610 A Systematic Study of Security of Industrial IoT 229Ravi Gedam and Surendra Rahamatkar10.1 Introduction 23010.2 Overview of Industrial Internet of Things (Smart Manufacturing) 23110.2.1 Key Enablers in Industry 4.0 23310.2.2 OPC Unified Architecture (OPC UA) 23410.3 Industrial Reference Architecture 23610.3.1 Arrowgead 23710.3.2 FIWARE 23710.3.3 Industrial Internet Reference Architecture (IIRA) 23810.3.4 Kaa IoT Platform 23810.3.5 Open Connectivity Foundation (OCF) 23910.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0) 23910.3.7 ThingsBoard 24010.3.8 ThingSpeak 24010.3.9 ThingWorx 24010.4 FIWARE Generic Enabler (FIWARE GE) 24110.4.1 Core Context Management GE 24110.4.2 NGSI Context Data Model 24210.4.3 IDAS IoT Agents 24410.4.3.1 IoT Agent-JSON 24610.4.3.2 IoT Agent-OPC UA 24710.4.3.3 Context Provider 24710.4.4 FIWARE for Smart Industry 24810.5 Discussion 24910.5.1 Solutions Adopting FIWARE 25010.5.2 IoT Interoperability Testing 25110.6 Conclusion 252References 25311 Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems 257Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee11.1 Introduction 25811.2 Popular Privacy Design Recommendations 25811.2.1 Dynamic Authorization 25811.2.2 End to End Security 25911.2.3 Enrollment and Authentication APIs 25911.2.4 Distributed Authorization 26011.2.5 Decentralization Authentication 26111.2.6 Interoperable Privacy Profiles 26111.3 Current Privacy Challenges in CPS 26211.4 Privacy Aware Design for CPS 26311.5 Limitations 26511.6 Converting Risks of Applying AI Into Advantages 26611.6.1 Proof of Recognition and De-Anonymization 26711.6.2 Segregation, Shamefulness, Mistakes 26711.6.3 Haziness and Bias of Profiling 26711.6.4 Abuse Arising From Information 26711.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem 26811.7 Conclusion and Future Scope 269References 27012 Exposing Security and Privacy Issues on Cyber-Physical Systems 273Keshav Kaushik12.1 Introduction to Cyber-Physical Systems (CPS) 27312.2 Cyber-Attacks and Security in CPS 27712.3 Privacy in CPS 28112.4 Conclusion & Future Trends in CPS Security 284References 28513 Applications of Cyber-Physical Systems 289Amandeep Kaur and Jyotir Moy Chatterjee13.1 Introduction 28913.2 Applications of Cyber-Physical Systems 29113.2.1 Healthcare 29113.2.1.1 Related Work 29313.2.2 Education 29513.2.2.1 Related Works 29513.2.3 Agriculture 29613.2.3.1 Related Work 29713.2.4 Energy Management 29813.2.4.1 Related Work 29913.2.5 Smart Transportation 30013.2.5.1 Related Work 30113.2.6 Smart Manufacturing 30113.2.6.1 Related Work 30313.2.7 Smart Buildings: Smart Cities and Smart Houses 30313.2.7.1 Related Work 30413.3 Conclusion 304References 305Index 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, Taylor’s University, Malaysia. R. Sujatha, PhD, is an associate professor in the School of Information Technology and Engineering in Vellore Institute of Technology, Vellore, India.

• Preface xvAcknowledgement xix1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things 1Ravi Gedam and Surendra Rahamatkar1.1 Introduction 21.2 Background of Industrial Internet of Things 31.3 Literature Review 61.4 The Proposed Methodology 131.5 Experimental Requirements 141.6 Conclusion 15References 162 Integration of Big Data Analytics Into Cyber-Physical Systems 19Nandhini R.S. and Ramanathan L.2.1 Introduction 192.2 Big Data Model for Cyber-Physical System 212.2.1 Cyber-Physical System Architecture 222.2.2 Big Data Analytics Model 222.3 Big Data and Cyber-Physical System Integration 232.3.1 Big Data Analytics and Cyber-Physical System 232.3.1.1 Integration of CPS With BDA 242.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics 242.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System 252.4 Storage and Communication of Big Data for Cyber-Physical System 262.4.1 Big Data Storage for Cyber-Physical System 272.4.2 Big Data Communication for Cyber-Physical System 282.5 Big Data Processing in Cyber-Physical System 292.5.1 Data Processing 292.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing 292.5.1.2 Clustering in Big Data 312.5.1.3 Clustering in Cyber-Physical System 322.5.2 Big Data Analytics 322.6 Applications of Big Data for Cyber-Physical System 332.6.1 Manufacturing 332.6.2 Smart Grids and Smart Cities 342.6.3 Healthcare 352.6.4 Smart Transportation 352.7 Security and Privacy 362.8 Conclusion 37References 383 Machine Learning: A Key Towards Smart Cyber-Physical Systems 43Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur3.1 Introduction 443.2 Different Machine Learning Algorithms 463.2.1 Performance Measures for Machine Learning Algorithms 483.2.2 Steps to Implement ML Algorithms 493.2.3 Various Platforms Available for Implementation 503.2.4 Applications of Machine Learning in Electrical Engineering 503.3 ML Use-Case in MATLAB 513.4 ML Use-Case in Python 563.4.1 ML Model Deployment 593.5 Conclusion 60References 604 Precise Risk Assessment and Management 63Ambika N.4.1 Introduction 644.2 Need for Security 654.2.1 Confidentiality 654.2.2 Integrity 664.2.3 Availability 664.2.4 Accountability 664.2.5 Auditing 674.3 Different Kinds of Attacks 674.3.1 Malware 674.3.2 Man-in-the Middle Assault 694.3.3 Brute Force Assault 694.3.4 Distributed Denial of Service 694.4 Literature Survey 704.5 Proposed Work 754.5.1 Objective 754.5.2 Notations Used in the Contribution 764.5.3 Methodology 764.5.4 Simulation and Analysis 784.6 Conclusion 80References 805 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment 85Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja5.1 Introduction 865.2 IoT Components, Layered Architectures, Security Threats 895.2.1 IoT Components 895.2.2 IoT Layered Architectures 905.2.2.1 3-Layer Architecture 915.2.2.2 4-Layer Architecture 915.2.2.3 5-Layer Architecture 935.2.3 Associated Threats in the Layers 935.2.3.1 Node Capture 935.2.3.2 Playback Attack 935.2.3.3 Fake Node Augmentation 935.2.3.4 Timing Attack 945.2.3.5 Bootstrap Attack 945.2.3.6 Jamming Attack 945.2.3.7 Kill Command Attack 945.2.3.8 Denial-of-Service (DoS) Attack 945.2.3.9 Storage Attack 945.2.3.10 Exploit Attack 955.2.3.11 Man-In-The-Middle (MITM) Attack 955.2.3.12 XSS Attack 955.2.3.13 Malicious Insider Attack 955.2.3.14 Malwares 955.2.3.15 Zero-Day Attack 955.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT 975.3.1 Taxonomy of DDoS Attacks 995.3.1.1 Architectural Model 995.3.1.2 Exploited Vulnerability 1005.3.1.3 Protocol Level 1015.3.1.4 Degree of Automation 1015.3.1.5 Scanning Techniques 1015.3.1.6 Propagation Mechanism 1025.3.1.7 Impact Over the Victim 1025.3.1.8 Rate of Attack 1035.3.1.9 Persistence of Agents 1035.3.1.10 Validity of Source Address 1035.3.1.11 Type of Victim 1035.3.1.12 Attack Traffic Distribution 1035.3.2 Working Mechanism of DDoS Attack 1045.4 Existing Solution Mechanisms Against DDoS Over IoT 1055.4.1 Detection Techniques 1055.4.2 Prevention Mechanisms 1085.5 Challenges and Research Directions 1135.6 Conclusion 115References 1156 Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges 123Bhimavarapu Usharani6.1 Introduction 1246.2 Phishing Threats 1246.2.1 Internet Fraud 1246.2.1.1 Electronic-Mail Fraud 1256.2.1.2 Phishing Extortion 1266.2.1.3 Extortion Fraud 1276.2.1.4 Social Media Fraud 1276.2.1.5 Tourism Fraud 1286.2.1.6 Excise Fraud 1296.2.2 Phishing 1296.3 Deep Learning Architectures 1316.3.1 Convolution Neural Network (CNN) Models 1316.3.1.1 Recurrent Neural Network 1316.3.1.2 Long Short-Term Memory (LSTM) 1346.4 Related Work 1356.4.1 Machine Learning Approach 1356.4.2 Neural Network Approach 1366.4.3 Deep Learning Approach 1386.5 Analysis Report 1396.6 Current Challenges 1406.6.1 File-Less Malware 1406.6.2 Crypto Mining 1406.7 Conclusions 140References 1417 Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks 147Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar7.1 Introduction 1487.2 Literature Review 1507.3 Classification of Attacks 1527.3.1 The Perception Layer Network Attacks 1527.3.2 Network Attacks on the Application Control Layer 1537.3.3 Data Transmission Layer Network Attacks 1537.3.3.1 Rogue Access Point 1547.3.3.2 ARP Spoofing 1557.3.3.3 DNS Spoofing 1577.3.3.4 mDNS Spoofing 1607.3.3.5 SSL Stripping 1617.4 Proposed Algorithm of Detection and Prevention 1627.4.1 ARP Spoofing 1627.4.2 Rogue Access Point and SSL Stripping 1687.4.3 DNS Spoofing 1697.5 Results and Discussion 1737.6 Conclusion and Future Scope 173References 1748 Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications 179Saurav S. and Arnab Ghosh8.1 Introduction 1798.2 Modeling of Fourth Order Interleaved Boost Converter 1818.2.1 Introduction to the Topology 1818.2.2 Modeling of FIBC 1828.2.2.1 Mode 1 Operation (0 to d1 Ts) 1828.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts) 1848.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts) 1868.2.2.4 Mode 4 Operation (d3 Ts to Ts) 1888.2.3 Averaging of the Model 1908.2.4 Small Signal Analysis 1908.3 Controller Design for FIBC 1938.3.1 PID Controller 1938.3.2 Type II Controller 1948.3.3 Type III Controller 1958.4 Computational Results 1978.5 Conclusion 204References 2059 Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management 209Somya Goyal9.1 Introduction 2109.1.1 RFID and IoT for Smart Inventory Management 2109.2 Benefits and Barriers in Implementation of RFID 2129.2.1 Benefits 2139.2.1.1 Routine Automation 2139.2.1.2 Improvement in the Visibility of Assets and Quick Availability 2159.2.1.3 SCM-Business Benefits 2159.2.1.4 Automated Lost and Found 2169.2.1.5 Smart Investment on Inventory 2179.2.1.6 Automated Patient Tracking 2179.2.2 Barriers 2189.2.2.1 RFID May Interfere With Medical Activities 2189.2.2.2 Extra Maintenance for RFID Tags 2189.2.2.3 Expense Overhead 2189.2.2.4 Interoperability Issues 2189.2.2.5 Security Issues 2189.3 IoT-Based Inventory Management—Case Studies 2189.4 Proposed Model for RFID-Based Hospital Management 2209.5 Conclusion and Future Scope 225References 22610 A Systematic Study of Security of Industrial IoT 229Ravi Gedam and Surendra Rahamatkar10.1 Introduction 23010.2 Overview of Industrial Internet of Things (Smart Manufacturing) 23110.2.1 Key Enablers in Industry 4.0 23310.2.2 OPC Unified Architecture (OPC UA) 23410.3 Industrial Reference Architecture 23610.3.1 Arrowgead 23710.3.2 FIWARE 23710.3.3 Industrial Internet Reference Architecture (IIRA) 23810.3.4 Kaa IoT Platform 23810.3.5 Open Connectivity Foundation (OCF) 23910.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0) 23910.3.7 ThingsBoard 24010.3.8 ThingSpeak 24010.3.9 ThingWorx 24010.4 FIWARE Generic Enabler (FIWARE GE) 24110.4.1 Core Context Management GE 24110.4.2 NGSI Context Data Model 24210.4.3 IDAS IoT Agents 24410.4.3.1 IoT Agent-JSON 24610.4.3.2 IoT Agent-OPC UA 24710.4.3.3 Context Provider 24710.4.4 FIWARE for Smart Industry 24810.5 Discussion 24910.5.1 Solutions Adopting FIWARE 25010.5.2 IoT Interoperability Testing 25110.6 Conclusion 252References 25311 Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems 257Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee11.1 Introduction 25811.2 Popular Privacy Design Recommendations 25811.2.1 Dynamic Authorization 25811.2.2 End to End Security 25911.2.3 Enrollment and Authentication APIs 25911.2.4 Distributed Authorization 26011.2.5 Decentralization Authentication 26111.2.6 Interoperable Privacy Profiles 26111.3 Current Privacy Challenges in CPS 26211.4 Privacy Aware Design for CPS 26311.5 Limitations 26511.6 Converting Risks of Applying AI Into Advantages 26611.6.1 Proof of Recognition and De-Anonymization 26711.6.2 Segregation, Shamefulness, Mistakes 26711.6.3 Haziness and Bias of Profiling 26711.6.4 Abuse Arising From Information 26711.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem 26811.7 Conclusion and Future Scope 269References 27012 Exposing Security and Privacy Issues on Cyber-Physical Systems 273Keshav Kaushik12.1 Introduction to Cyber-Physical Systems (CPS) 27312.2 Cyber-Attacks and Security in CPS 27712.3 Privacy in CPS 28112.4 Conclusion & Future Trends in CPS Security 284References 28513 Applications of Cyber-Physical Systems 289Amandeep Kaur and Jyotir Moy Chatterjee13.1 Introduction 28913.2 Applications of Cyber-Physical Systems 29113.2.1 Healthcare 29113.2.1.1 Related Work 29313.2.2 Education 29513.2.2.1 Related Works 29513.2.3 Agriculture 29613.2.3.1 Related Work 29713.2.4 Energy Management 29813.2.4.1 Related Work 29913.2.5 Smart Transportation 30013.2.5.1 Related Work 30113.2.6 Smart Manufacturing 30113.2.6.1 Related Work 30313.2.7 Smart Buildings: Smart Cities and Smart Houses 30313.2.7.1 Related Work 30413.3 Conclusion 304References 305Index 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, Taylor’s University, Malaysia. R. Sujatha, PhD, is an associate professor in the School of Information Technology and Engineering in Vellore Institute of Technology, Vellore, India.

• Preface xvAcknowledgement xix1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things 1Ravi Gedam and Surendra Rahamatkar1.1 Introduction 21.2 Background of Industrial Internet of Things 31.3 Literature Review 61.4 The Proposed Methodology 131.5 Experimental Requirements 141.6 Conclusion 15References 162 Integration of Big Data Analytics Into Cyber-Physical Systems 19Nandhini R.S. and Ramanathan L.2.1 Introduction 192.2 Big Data Model for Cyber-Physical System 212.2.1 Cyber-Physical System Architecture 222.2.2 Big Data Analytics Model 222.3 Big Data and Cyber-Physical System Integration 232.3.1 Big Data Analytics and Cyber-Physical System 232.3.1.1 Integration of CPS With BDA 242.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics 242.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System 252.4 Storage and Communication of Big Data for Cyber-Physical System 262.4.1 Big Data Storage for Cyber-Physical System 272.4.2 Big Data Communication for Cyber-Physical System 282.5 Big Data Processing in Cyber-Physical System 292.5.1 Data Processing 292.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing 292.5.1.2 Clustering in Big Data 312.5.1.3 Clustering in Cyber-Physical System 322.5.2 Big Data Analytics 322.6 Applications of Big Data for Cyber-Physical System 332.6.1 Manufacturing 332.6.2 Smart Grids and Smart Cities 342.6.3 Healthcare 352.6.4 Smart Transportation 352.7 Security and Privacy 362.8 Conclusion 37References 383 Machine Learning: A Key Towards Smart Cyber-Physical Systems 43Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur3.1 Introduction 443.2 Different Machine Learning Algorithms 463.2.1 Performance Measures for Machine Learning Algorithms 483.2.2 Steps to Implement ML Algorithms 493.2.3 Various Platforms Available for Implementation 503.2.4 Applications of Machine Learning in Electrical Engineering 503.3 ML Use-Case in MATLAB 513.4 ML Use-Case in Python 563.4.1 ML Model Deployment 593.5 Conclusion 60References 604 Precise Risk Assessment and Management 63Ambika N.4.1 Introduction 644.2 Need for Security 654.2.1 Confidentiality 654.2.2 Integrity 664.2.3 Availability 664.2.4 Accountability 664.2.5 Auditing 674.3 Different Kinds of Attacks 674.3.1 Malware 674.3.2 Man-in-the Middle Assault 694.3.3 Brute Force Assault 694.3.4 Distributed Denial of Service 694.4 Literature Survey 704.5 Proposed Work 754.5.1 Objective 754.5.2 Notations Used in the Contribution 764.5.3 Methodology 764.5.4 Simulation and Analysis 784.6 Conclusion 80References 805 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment 85Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja5.1 Introduction 865.2 IoT Components, Layered Architectures, Security Threats 895.2.1 IoT Components 895.2.2 IoT Layered Architectures 905.2.2.1 3-Layer Architecture 915.2.2.2 4-Layer Architecture 915.2.2.3 5-Layer Architecture 935.2.3 Associated Threats in the Layers 935.2.3.1 Node Capture 935.2.3.2 Playback Attack 935.2.3.3 Fake Node Augmentation 935.2.3.4 Timing Attack 945.2.3.5 Bootstrap Attack 945.2.3.6 Jamming Attack 945.2.3.7 Kill Command Attack 945.2.3.8 Denial-of-Service (DoS) Attack 945.2.3.9 Storage Attack 945.2.3.10 Exploit Attack 955.2.3.11 Man-In-The-Middle (MITM) Attack 955.2.3.12 XSS Attack 955.2.3.13 Malicious Insider Attack 955.2.3.14 Malwares 955.2.3.15 Zero-Day Attack 955.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT 975.3.1 Taxonomy of DDoS Attacks 995.3.1.1 Architectural Model 995.3.1.2 Exploited Vulnerability 1005.3.1.3 Protocol Level 1015.3.1.4 Degree of Automation 1015.3.1.5 Scanning Techniques 1015.3.1.6 Propagation Mechanism 1025.3.1.7 Impact Over the Victim 1025.3.1.8 Rate of Attack 1035.3.1.9 Persistence of Agents 1035.3.1.10 Validity of Source Address 1035.3.1.11 Type of Victim 1035.3.1.12 Attack Traffic Distribution 1035.3.2 Working Mechanism of DDoS Attack 1045.4 Existing Solution Mechanisms Against DDoS Over IoT 1055.4.1 Detection Techniques 1055.4.2 Prevention Mechanisms 1085.5 Challenges and Research Directions 1135.6 Conclusion 115References 1156 Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges 123Bhimavarapu Usharani6.1 Introduction 1246.2 Phishing Threats 1246.2.1 Internet Fraud 1246.2.1.1 Electronic-Mail Fraud 1256.2.1.2 Phishing Extortion 1266.2.1.3 Extortion Fraud 1276.2.1.4 Social Media Fraud 1276.2.1.5 Tourism Fraud 1286.2.1.6 Excise Fraud 1296.2.2 Phishing 1296.3 Deep Learning Architectures 1316.3.1 Convolution Neural Network (CNN) Models 1316.3.1.1 Recurrent Neural Network 1316.3.1.2 Long Short-Term Memory (LSTM) 1346.4 Related Work 1356.4.1 Machine Learning Approach 1356.4.2 Neural Network Approach 1366.4.3 Deep Learning Approach 1386.5 Analysis Report 1396.6 Current Challenges 1406.6.1 File-Less Malware 1406.6.2 Crypto Mining 1406.7 Conclusions 140References 1417 Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks 147Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar7.1 Introduction 1487.2 Literature Review 1507.3 Classification of Attacks 1527.3.1 The Perception Layer Network Attacks 1527.3.2 Network Attacks on the Application Control Layer 1537.3.3 Data Transmission Layer Network Attacks 1537.3.3.1 Rogue Access Point 1547.3.3.2 ARP Spoofing 1557.3.3.3 DNS Spoofing 1577.3.3.4 mDNS Spoofing 1607.3.3.5 SSL Stripping 1617.4 Proposed Algorithm of Detection and Prevention 1627.4.1 ARP Spoofing 1627.4.2 Rogue Access Point and SSL Stripping 1687.4.3 DNS Spoofing 1697.5 Results and Discussion 1737.6 Conclusion and Future Scope 173References 1748 Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications 179Saurav S. and Arnab Ghosh8.1 Introduction 1798.2 Modeling of Fourth Order Interleaved Boost Converter 1818.2.1 Introduction to the Topology 1818.2.2 Modeling of FIBC 1828.2.2.1 Mode 1 Operation (0 to d1 Ts) 1828.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts) 1848.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts) 1868.2.2.4 Mode 4 Operation (d3 Ts to Ts) 1888.2.3 Averaging of the Model 1908.2.4 Small Signal Analysis 1908.3 Controller Design for FIBC 1938.3.1 PID Controller 1938.3.2 Type II Controller 1948.3.3 Type III Controller 1958.4 Computational Results 1978.5 Conclusion 204References 2059 Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management 209Somya Goyal9.1 Introduction 2109.1.1 RFID and IoT for Smart Inventory Management 2109.2 Benefits and Barriers in Implementation of RFID 2129.2.1 Benefits 2139.2.1.1 Routine Automation 2139.2.1.2 Improvement in the Visibility of Assets and Quick Availability 2159.2.1.3 SCM-Business Benefits 2159.2.1.4 Automated Lost and Found 2169.2.1.5 Smart Investment on Inventory 2179.2.1.6 Automated Patient Tracking 2179.2.2 Barriers 2189.2.2.1 RFID May Interfere With Medical Activities 2189.2.2.2 Extra Maintenance for RFID Tags 2189.2.2.3 Expense Overhead 2189.2.2.4 Interoperability Issues 2189.2.2.5 Security Issues 2189.3 IoT-Based Inventory Management—Case Studies 2189.4 Proposed Model for RFID-Based Hospital Management 2209.5 Conclusion and Future Scope 225References 22610 A Systematic Study of Security of Industrial IoT 229Ravi Gedam and Surendra Rahamatkar10.1 Introduction 23010.2 Overview of Industrial Internet of Things (Smart Manufacturing) 23110.2.1 Key Enablers in Industry 4.0 23310.2.2 OPC Unified Architecture (OPC UA) 23410.3 Industrial Reference Architecture 23610.3.1 Arrowgead 23710.3.2 FIWARE 23710.3.3 Industrial Internet Reference Architecture (IIRA) 23810.3.4 Kaa IoT Platform 23810.3.5 Open Connectivity Foundation (OCF) 23910.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0) 23910.3.7 ThingsBoard 24010.3.8 ThingSpeak 24010.3.9 ThingWorx 24010.4 FIWARE Generic Enabler (FIWARE GE) 24110.4.1 Core Context Management GE 24110.4.2 NGSI Context Data Model 24210.4.3 IDAS IoT Agents 24410.4.3.1 IoT Agent-JSON 24610.4.3.2 IoT Agent-OPC UA 24710.4.3.3 Context Provider 24710.4.4 FIWARE for Smart Industry 24810.5 Discussion 24910.5.1 Solutions Adopting FIWARE 25010.5.2 IoT Interoperability Testing 25110.6 Conclusion 252References 25311 Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems 257Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee11.1 Introduction 25811.2 Popular Privacy Design Recommendations 25811.2.1 Dynamic Authorization 25811.2.2 End to End Security 25911.2.3 Enrollment and Authentication APIs 25911.2.4 Distributed Authorization 26011.2.5 Decentralization Authentication 26111.2.6 Interoperable Privacy Profiles 26111.3 Current Privacy Challenges in CPS 26211.4 Privacy Aware Design for CPS 26311.5 Limitations 26511.6 Converting Risks of Applying AI Into Advantages 26611.6.1 Proof of Recognition and De-Anonymization 26711.6.2 Segregation, Shamefulness, Mistakes 26711.6.3 Haziness and Bias of Profiling 26711.6.4 Abuse Arising From Information 26711.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem 26811.7 Conclusion and Future Scope 269References 27012 Exposing Security and Privacy Issues on Cyber-Physical Systems 273Keshav Kaushik12.1 Introduction to Cyber-Physical Systems (CPS) 27312.2 Cyber-Attacks and Security in CPS 27712.3 Privacy in CPS 28112.4 Conclusion & Future Trends in CPS Security 284References 28513 Applications of Cyber-Physical Systems 289Amandeep Kaur and Jyotir Moy Chatterjee13.1 Introduction 28913.2 Applications of Cyber-Physical Systems 29113.2.1 Healthcare 29113.2.1.1 Related Work 29313.2.2 Education 29513.2.2.1 Related Works 29513.2.3 Agriculture 29613.2.3.1 Related Work 29713.2.4 Energy Management 29813.2.4.1 Related Work 29913.2.5 Smart Transportation 30013.2.5.1 Related Work 30113.2.6 Smart Manufacturing 30113.2.6.1 Related Work 30313.2.7 Smart Buildings: Smart Cities and Smart Houses 30313.2.7.1 Related Work 30413.3 Conclusion 304References 305Index 311