Cyber-physical systems : foundations and techniques / edited by Uzzal Sharma [and five others]

Includes bibliographical references and index.

Table of 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

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

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

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

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

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

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

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

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

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.

About the Author

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.