Preface xiii 1 Face Recognition–Based Surveillance System: A New Paradigm for Criminal Profiling 1Payal Singh, Sneha Gupta, Vipul Gupta, Piyush Kuchhal and Arpit Jain 1.1 Introduction 1 1.2 Image Processing 6 1.3 Deep Learning 7 1.3.1 Neural Network 9 1.3.2 Application of Neural Network in Face Recognition 10 1.4 Methodology 10 1.4.1 Face Recognition 10 1.4.2 Open CV 11 1.4.3 Block Diagram 11 1.4.4 Essentials Needed 12 1.4.5 Website 12 1.4.6 Hardware 12 1.4.7 Procedure 12 1.5 Conclusion 16 References 17 2 Smart Healthcare Monitoring System: An IoT-Based Approach 19Paranjeet Kaur 2.1 Introduction 19 2.2 Healthcare at Clinics 21 2.3 Remote Healthcare 21 2.4 Technological Framework 21 2.5 Standard UIs, Shows, and User Requirements 23 2.5.1 Advantages 23 2.5.2 Application 23 2.6 Cloud-Based Health Monitoring Using IoT 24 2.7 Information Acquisition 24 2.8 The Processing of Cloud 25 2.9 IoT-Based Health Monitoring Using Raspberry Pi 25 2.10 IoT-Based Health Monitoring Using RFID 26 2.10.1 Sensor Layer 27 2.10.2 Network Layer 28 2.10.3 Service Layer 28 2.11 Arduino and IoT-Based Health Monitoring System 28 2.12 IoT-Based Health Monitoring System Using ECG Signal 29 2.12.1 System Model 30 2.12.2 Framework 30 2.13 IoT-Based Health Monitoring System Using Android App 31 2.13.1 Transferring the Information to the Cloud 33 2.13.2 Application Controls 33 2.14 Conclusion and Future Perspectives 33 References 34 3 Design of Gesture-Based Hand Gloves Using Arduino UNO: A Grace to Abled Mankind 37Harpreet Singh Bedi, Dekkapati Vinit Raju, Nandyala Meghanath Reddy C. Partha Sai Kumar and Mandla Ravi Varma 3.1 Introduction 38 3.1.1 Block Diagram 38 3.1.2 The Proposed New Design 39 3.1.3 Circuit Diagram 40 3.2 Result and Discussion 40 3.2.1 Data Analysis 41 3.3 Conclusion 41 3.4 Future Scope 42 References 42 4 Playing With Genes: A Pragmatic Approach in Genetic Engineering 45Prerna Singh and Dolly Sharma 4.1 Introduction 46 4.2 Literature Review 47 4.3 Methodology 48 4.3.1 Plasmid Method 48 4.3.2 The Vector Method 49 4.3.3 The Biolistic Method 49 4.4 Food and Agriculture 50 4.5 Impact on Farmers 53 4.6 Diseases: Gene Editing and Curing 54 4.7 Conclusion 56 4.8 Future Scope 56 References 57 5 Digital Investigative Model in IoT: Forensic View 59Suryapratap Ray and Tejasvi Bhatia 5.1 Introduction 59 5.1.1 Artificial Neural Network 60 5.2 Application of AI for Different Purposes in Forensic Science 61 5.2.1 Artificial Intelligence for Drug Toxicity and Safety 61 5.2.2 Crime Scene Reconstruction 62 5.2.3 Sequence or Pattern Recognition 62 5.2.4 Repositories Building 63 5.2.5 Establishment of Connection Among the Investigating Team 63 5.2.6 Artificial Intelligence and Expert System in Mass Spectrometry 63 5.2.7 AI in GPS Navigation 65 5.3 Future of AI 66 5.4 Challenges While Implementing AI 67 5.4.1 Unexplainability of AI 67 5.4.2 AI Anti-Forensics 67 5.4.3 Connection Interruption Between the Cyber Forensics and AI Communities 67 5.4.4 Data Analysis and Security 68 5.4.5 Creativity 68 5.5 Conclusion 68 References 69 6 Internet of Things Mobility Forensics 73Shipra Rohatgi, Aman Sharma and Bhavya Sharma 6.1 Introduction 74 6.2 Smart Device and IoT 75 6.3 Relation of Internet of Things with Mobility Forensics 76 6.3.1 Cyber Attack on IoT Data 77 6.3.2 Data Recovery from IoT Devices 78 6.3.3 Scenario-Based Analysis of IoT Data as Evidence 79 6.4 Mobility Forensics IoT Investigation Model 80 6.5 Internet of Things Mobility Forensics: A Source of Information 82 6.6 Drawbacks in IoT Devices Data Extraction 82 6.7 Future Perspective of Internet of Things Mobility Forensics 84 6.8 Conclusion 84 References 85 7 A Generic Digital Scientific Examination System for Internet of Things 87Shipra Rohatgi and Sakshi Shrivastava 7.1 Introduction 88 7.2 Internet of Things 89 7.3 IoT Architecture 91 7.4 Characteristics of IoT 92 7.5 IoT Security Challenges and Factors of Threat 94 7.5.1 Effects of IoT Security Breach 95 7.6 Role of Digital Forensics in Cybercrime Investigation for IoT 96 7.6.1 IoT in Digital Forensic 96 7.6.2 Digital Forensics Investigation Framework for IoT Devices 98 7.6.3 Road Map for Issues in IoT Forensics 99 7.7 IoT Security Steps 102 7.7.1 How to Access IoT Security 103 7.8 Conclusion 107 References 108 8 IoT Sensors: Security in Network Forensics 111D. Karthika 8.1 Introduction 111 8.2 Cybersecurity Versus IoT Security and Cyber-Physical Systems 112 8.3 The IoT of the Future and the Need to Secure 114 8.3.1 The Future—Cognitive Systems and the IoT 114 8.4 Security Engineering for IoT Development 115 8.5 Building Security Into Design and Development 115 8.6 Security in Agile Developments 116 8.7 Focusing on the IoT Device in Operation 117 8.8 Cryptographic Fundamentals for IoT Security Engineering 118 8.8.1 Types and Uses of Cryptographic Primitives in the IoT 118 8.8.1.1 Encryption and Decryption 119 8.8.1.2 Symmetric Encryption 120 8.8.1.3 Asymmetric Encryption 121 8.8.1.4 Hashes 122 8.8.1.5 Digital Signatures 123 8.8.1.6 Symmetric (MACS) 123 8.8.1.7 Random Number Generation 124 8.8.1.8 Cipher Suites 125 8.9 Cloud Security for the IoT 125 8.9.1 Asset/Record Organization 126 8.9.2 Service Provisioning, Billing, and Entitlement Management 126 8.9.3 Real-Rime Monitoring 126 8.9.4 Sensor Coordination 127 8.9.5 Customer Intelligence and Marketing 127 8.9.6 Information Sharing 127 8.9.7 Message Transport/Broadcast 128 8.10 Conclusion 128 References 129 9 Xilinx FPGA and Xilinx IP Cores: A Boon to Curb Digital Crime 131B. Khaleelu Rehman, G. Vallathan, Vetriveeran Rajamani and Salauddin Mohammad 9.1 Introduction 132 9.2 Literature Review 132 9.3 Proposed Work 132 9.4 Xilinx IP Core Square Root 136 9.5 RTL View of the 8-Bit Multiplier 140 9.5.1 Eight-Bit Multiplier Simulation Results Using IP Core 144 9.6 RTL View of 8-Bit Down Counter 145 9.6.1 Eight-Bit Down Counter Simulation Results 145 9.7 Up/Down Counter Simulation Results 149 9.8 Square Root Simulation Results 150 9.9 Hardware Device Utilization Reports of Binary Down Counter 154 9.10 Comparison of Proposed and Existing Work for Binary Up/Down Counter 156 9.10.1 Power Analysis of Binary Up/Down Counter 159 9.11 Conclusion 160 References 160 10 Human-Robot Interaction: An Artificial Cognition-Based Study for Criminal Investigations 163Deepansha Adlakha and Dolly Sharma 10.1 Introduction 164 10.1.1 Historical Background 165 10.2 Methodology 167 10.2.1 Deliberative Architecture and Knowledge Model 167 10.2.1.1 Natural Mind 168 10.2.1.2 Prerequisites for Developing the Mind of the Social Robots 169 10.2.1.3 Robot Control Paradigms 169 10.3 Architecture Models for Robots 170 10.4 Cognitive Architecture 171 10.4.1 Taxonomy of Cognitive Architectures 172 10.4.1.1 Symbolic Architectures 172 10.4.1.2 The Emergent or the Connectionist Architecture 173 10.4.1.3 The Hybrid Architecture 173 10.4.2 Cognitive Skills 173 10.4.2.1 Emotions 173 10.4.2.2 Dialogue for Socially Interactive Communication 175 10.4.2.3 Memory in Social Robots 178 10.4.2.4 Learning 180 10.4.2.5 Perception 181 10.5 Challenges in the Existing Social Robots and the Future Scopes 187 10.5.1 Sensors Technology 187 10.5.2 Understanding and Learning from the Operator 187 10.5.3 Architectural Design 188 10.5.4 Testing Phase 189 10.5.5 Credible, Legitimate, and Social Aspects 189 10.5.6 Automation in Digital Forensics 190 10.6 Conclusion 190 10.7 Robots in Future Pandemics 194 References 194 11 VANET: An IoT Forensic-Based Model for Maintaining Chain of Custody 199Manoj Sindhwani, Charanjeet Singh and Rajeshwar Singh 11.1 Introduction 200 11.2 Cluster Performance Parameters 201 11.3 Routing Protocols in VANET 202 11.3.1 Performance Metrics 202 11.3.2 Proposed Cluster Head Selection Algorithm 203 11.4 Internet of Vehicles 205 11.5 IoT Forensic in Vehicular Ad Hoc Networks 206 11.6 Conclusion 207 References 207 12 Cognitive Radio Networks: A Merit for Teleforensics 211Yogita Thareja, Kamal Kumar Sharma and Parulpreet Singh 12.1 Introduction 212 12.1.1 Integration of WSN with Psychological Radio 213 12.1.2 Characteristics of Cognitive Radio 214 12.2 Contribution of Work 216 12.2.1 Push-to-Talk 218 12.2.2 Digital Forensic–Radio Communication Equipment 219 12.2.3 Energy Harvesting Network 220 12.2.4 Challenges with the Use of Clusters in Cognitive Radio Networks 220 12.3 Conclusion and Future Scope 221 Acknowledgement 221 References 222 13 Fingerprint Image Identification System: An Asset for Security of Bank Lockers 227Mahendra, Apoorva, Shyam, Pavan and Harpreet Bedi 13.1 Introduction 227 13.1.1 Design Analysis 230 13.2 Result and Discussion 231 13.3 Conclusion 232 13.4 Future Scope 234 References 235 14 IoT Forensics: Interconnection and Sensing Frameworks 237Nidhi Sagarwal 14.1 Introduction 237 14.2 The Need for IoT Forensics 240 14.3 Various Types of Evidences Encountered 242 14.4 Protocols and Frameworks in IoT Forensics 242 14.5 IoT Forensics Process Model 243 14.6 Suggestive Solutions 248 14.7 Conclusion 249 References 249 15 IoT Forensics: A Pernicious Repercussions 255Gift Chimkonda Chichele 15.1 Introduction: Challenges in IoT Forensics 255 15.2 Scope of the Compromise and Crime Scene Reconstruction 256 15.3 Device and Data Proliferation 256 15.4 Multiple Data Location and Jurisdiction Challenges 256 15.5 Device Type 257 15.6 Lack of Training and Weak Knowledge Management 257 15.7 Data Encryption 258 15.8 Heterogeneous Software and/or Hardware Specifications 258 15.9 Privacy and Ethical Considerations by Accessing Personal Data 258 15.10 Lack of a Common Forensic Model in IoT Devices 259 15.11 Securing the Chain of Custody 259 15.12 Lifespan Limitation 259 15.13 The Cloud Forensic Problem 259 15.14 The Minimum or Maximum Period in Which Data is Stored in the Cloud 260 15.15 Evidence Analysis and Correlation 260 15.16 Conclusion 260 References 262 About the Editors 263 Index 265 

It pays to be ahead of the criminal, and this book helps organizations and people to create a path to achieve this goal. The book discusses applications and challenges professionals encounter in the burgeoning field of IoT forensics. IoT forensics attempts to align its workflow to that of any forensics practice—investigators identify, interpret, preserve, analyze and present any relevant data. As with any investigation, a timeline is constructed, and, with the aid of smart devices providing data, investigators might be able to capture much more specific data points than in a traditional crime. However, collecting this data can often be a challenge, as it frequently doesn’t live on the device itself, but rather in the provider’s cloud platform. If you can get the data off the device, you’ll have to employ one of a variety of methods given the diverse nature of IoT devices hardware, software, and firmware. So, while robust and insightful data is available, acquiring it is no small undertaking. Digital Forensics and Internet of Things encompasses: State-of-the-art research and standards concerning IoT forensics and traditional digital forensicsCompares and contrasts IoT forensic techniques with those of traditional digital forensics standardsIdentifies the driving factors of the slow maturation of IoT forensic standards and possible solutionsApplies recommended standards gathered from IoT forensic literature in hands-on experiments to test their effectiveness across multiple IoT devicesProvides educated recommendations on developing and establishing IoT forensic standards, research, and areas that merit further study. Audience Researchers and scientists in forensic sciences, computer sciences, electronics engineering, embedded systems, information technology.