Preface xvii
Part I: Intelligent Cloud-IoT Security 1
1 Intrusion Detection in Cloud-IoT Systems: Challenges and Opportunities 3
Anindita Raychaudhuri and Inadyuti Dutt
1.1 Introduction 4
1.2 Overview of Cloud IoT Systems 5
1.3 Challenges in Cloud IoT Systems 5
1.4 Security Issues in Cloud Systems 6
1.5 Evolution of Intrusion Detection Systems 9
1.5.1 Evolution of IDTs in IoT-Cloud Systems 9
1.5.2 Comparative Analysis of Intrusion Detection Systems 11
1.6 Techniques and Algorithms for Intrusion Detection 12
1.7 Applications Areas of Intrusion Detection in Cloud-IoT Systems 14
1.8 Future Directions and Research Opportunities 22
1.9 Conclusion 23
References 24
2 Applications of Artificial Intelligence for Early Detection of Cyber Threats in Cloud Networks for IoT Devices: A Sentinel Analysis 31
Kaushiki Chatterjee and Soumen Santra
2.1 Introduction 32
2.2 Implementing Protective Measures and Following Best Practices to Mitigate Threats from IoCST 35
2.3 Utilizing Diffie-Hellman for Enhancing IoT Security 41
2.4 Utilizing Machine Learning to Enhance Security in the Realm of IoT 42
2.5 Future 43
2.6 Conclusion 44
References 45
3 Securing the Interconnected: AI-Driven Strategies for Dynamic Cloud-IoT Ecosystem 49
Ayan Banerjee and Anirban Kundu
3.1 Introduction 50
3.1.1 Overview 50
3.1.2 Aim 51
3.1.3 Scope 51
3.1.4 Motivation 51
3.1.5 Organization 52
3.2 Literature Review 52
3.2.1 Past Researches 52
3.2.2 Challenges 53
3.3 CA Based MCMS Framework for System Allocation Using Memory Capacity Analysis 53
3.4 Cloud-Based Communication between Administrator Module and Controller Module for Maintaining IoT Ecosystem Capacity 58
3.5 Functional Communication between User Module and Controller Module for Query Analysis 60
3.6 Controller Design for Measuring System Capacity Using CA 63
3.6.1 CA Based Controller Design for IoT Ecosystem's Performance Sustainability 64
3.6.2 CA Based Controller Design for User Query Analysis 68
3.7 Analytical Discussion 73
3.7.1 Connection Demand Analysis Based on Connections between Web Server and Database Server 73
3.7.2 Server Load Analysis Based on Connections between Web Server and Database Server 74
3.7.3 HDD Capacity Analysis 74
3.7.4 RAM Capacity Analysis 74
3.7.5 Memory Capacity Analysis 75
3.7.6 System Reliability Analysis 75
3.8 Theoretical Discussion 75
3.8.1 Theoretical Perspective on Server Load Evaluation 75
3.8.2 Theoretical Examination of HDD Capacity Analysis 80
3.8.3 Theoretical Examination of RAM Capacity Analysis 81
3.8.4 Theoretical Foundation on Memory Capacity Analysis 82
3.8.5 Theoretical Discussion on System Reliability 83
3.9 Experimental Discussion 84
3.9.1 Overview 84
3.9.2 Experimental Setup 84
3.9.3 Time Complexity Analysis 85
3.9.4 System Load Analysis 86
3.9.5 System Proficiency Analysis Using Different Factors 88
3.10 Comparison 90
3.11 Conclusion 94
Acknowledgment 95
References 95
4 Navigating the Fog AI-Driven Resilience and Privacy Preservation in Cloud IoT Environments 99
Bhupendra Panchal, Sarah Joby David, Ritika Singh, Manini Chhabra, Ajay Sharma and Tarannum Khan
4.1 Introduction 100
4.2 Literature Review 103
4.2.1 Cloud and IoT: Challenges and Opportunities 103
4.2.2 AI-Driven Resilience in Fog and Cloud IoT Environments 103
4.2.3 Privacy Preservation in AI-Driven Cloud IoT Systems 104
4.2.4 Security Concerns and AI Mitigation Strategies 104
4.3 Proposed Work 105
4.4 Experimental Setup 109
4.4.1 Tools 109
4.4.2 Simulation 110
4.4.3 Dataset 110
4.5 Experimental Results 111
4.5.1 Privacy Breach Risk Comparison 111
4.5.2 Latency Comparison 112
4.5.3 Bandwidth Usage Comparison 112
4.5.4 Model Accuracy and Resilience Comparison 113
4.6 Conclusion 115
References 115
5 Learning Safeguards: Leveraging Machine Learning for Anomaly Detection in Cloud - IoT Networks 119
Swastika Kayal and Soumen Santra
5.1 Introduction 120
5.1.1 Cloud Security 122
5.1.2 Adhoc Network 122
5.2 Background and Literature Survey 123
5.3 Methodology 124
5.3.1 Deviation Detection System 124
5.3.1.1 Anomaly Detection in Network Using Optimized Kernel-SVM 125
5.3.1.2 Anomaly Detection in Network Using Hierarchical Trees 126
5.3.2 Intrusion Detection System 126
5.3.3 Behavioral Malware Detection Techniques 128
5.3.4 Bayesian Network for Predictive Threat Modeling 130
5.4 Comparative Analysis 132
5.4.1 Comparative Analysis of Outlier Detection Techniques 132
5.4.2 Supervised Learning: Kernel SVM 132
5.4.2.1 Pros 132
5.4.2.2 Cons 132
5.4.3 Supervised Learning: Hierarchical Trees 132
5.4.3.1 Pros 133
5.4.3.2 Cons 133
5.4.4 Deep Learning: Spatial Feature Learner (SFL) 133
5.4.4.1 Pros 133
5.4.4.2 Cons 133
5.4.5 Deep Learning: Recurrent Neural Networks (RNN) 134
5.4.5.1 Pros 134
5.4.5.2 Cons 134
5.4.6 Bayesian Networks for Predictive Threat Modeling 134
5.4.6.1 Pros 134
5.4.6.2 Cons 134
5.5 Results and Discussion 135
5.5.1 Dataset Link 136
5.5.2 Dataset Table 136
5.5.3 Output 136
5.6 Future Work 138
5.6.1 Transfer Learning in IoT Anomaly Detection 139
5.6.2 Semi-Supervised Learning for IoT 139
5.6.3 Data Augmentation Techniques for IoT Networks 140
5.6.4 Continuous Learning and Adaptation 140
5.6.5 Scalability and Real-Time Detection 140
5.7 Conclusion 141
References 141
6 Smart Shields: Machine Learning Approaches for Adaptive Defense in Cloud-IoT Security 143
Bhupendra Panchal, Aafiya Choudhary, Ashish Anand, Ajay Sharma and Tarannum Khan
6.1 Introduction 144
6.1.1 Motivation of the Study 145
6.1.2 Problem Statement 145
6.2 Literature Review 146
6.3 Proposed Methodology 148
6.3.1 Data Collection and Simulation 149
6.3.2 Layered Architecture 149
6.3.3 Model Adaptation and Defense Mechanisms 150
6.4 Experimental Result 151
6.4.1 Hardware and Network Environment 151
6.4.2 Datasets 152
6.4.3 ml Algorithms 152
6.4.4 Threat Simulation 152
6.4.5 Adaptive Defense Mechanism 153
6.5 Result Analysis 153
6.5.1 Detection Accuracy 153
6.5.2 Latency 154
6.5.3 Power Consumption 154
6.5.4 Model Scalability 155
6.5.5 Adaptability 156
6.6 Conclusion 157
References 158
7 Real Time Threats Prediction and Security Issues in Cloud and Internet of Things System: The AI and ML Context 161
Nilanjan Das
7.1 Introduction 162
7.2 Objectives 163
7.3 Methodology 163
7.4 Fundamentals of Cyber Security Issues 165
7.5 Fundamentals of IoT in Association with Cloud Computing 167
7.6 Foundation of Artificial Intelligence and Machine Learning 170
7.7 Cyber Threats and Intrusion Detection Using AI and ml 174
7.8 Real Time Threat Detection and Prediction on Cloud IoT Platform in the Context of Artificial Intelligence 175
7.9 Core Findings 181
7.10 Conclusion and Future Work 182
Acknowledgement 182
References 183
8 Deep Learning Driven Heteromorphic Block Cipher (DL-HBC) Framework for Asynchronous Data Transmission in Heterogeneous Cloud Based Network 189
Nivedita Ray, Shreya Kumari, Ankita Bera, Shruti Singh and Anirban Kundu
8.1 Introduction 190
8.1.1 Overview 190
8.1.2 Literature Survey 191
8.1.3 Aim 193
8.1.4 Scope 194
8.1.5 Motivation 194
8.1.6 Organization 194
8.2 System Design and Architecture for Heteromorphic DLE 194
8.3 Procedure for Heteromorphic DLE 195
8.4 Detailed Procedural Explanation for Design Framework 200
8.5 Analysis on Asynchronous Data Transmission 201
8.6 Experimental Observations 206
8.6.1 Experimental Setup 206
8.6.2 Experimental Results 206
8.6.3 Comparative Analysis 206
8.6.4 Cost Analysis 209
8.7 Conclusion 220
Acknowledgment 221
References 221
Part II: Intelligent Intrusion Detection for Cloud-IoT System 225
9 Deep Learning Insights into Defending Against Adversarial Attacks in IoT Systems 227
J. Ramkumar and S. Vetrivel
9.1 Introduction 228
9.1.1 Overview of Adversarial Attacks on IoT Systems 228
9.1.2 Role of Deep Learning in Enhancing IoT Security 229
9.1.3 Review Literature Nature of Adversarial Attacks 230
9.1.4 Definition and Characteristics 230
9.1.5 Common Techniques Used in Attacks 231
9.1.6 Impact on IoT Systems and Devices 231
9.2 IoT System Vulnerabilities 232
9.2.1 Security Flaws in IoT Devices 233
9.2.2 Network Vulnerabilities 233
9.2.3 Exploitation Methods and Scenarios 234
9.3 Deep Learning Approaches 234
9.3.1 Overview of Deep Learning Models 235
9.3.2 Specific Algorithms for Security 236
9.3.3 Training and Validation of Models 236
9.4 Defense Mechanisms 237
9.4.1 Detection of Adversarial Attacks 238
9.4.2 Real-Time Threat Response 238
9.4.3 Mitigation and Prevention Strategies 239
9.5 Integration with IoT Security Frameworks 239
9.5.1 System Design Considerations 240
9.5.2 Scalability and Performance Issues 240
9.5.3 Practical Implementation Steps 241
9.6 Recent Advances and Future Trends 242
9.6.1 Innovations in Deep Learning for Security 242
9.6.2 Future Research Directions 245
9.7 Conclusion 246
9.7.1 Key Takeaways 246
9.7.2 Implications for IoT Security and Deep Learning Applications 247
References 248
10 Federated Learning for Intrusion Detection in Edge Computing for Cloud IoT Systems 251
Krupali Gosai, Hansa Vaghela, Yogeshwar Prajapati and Om Prakash Suthar
10.1 Introduction 252
10.1.1 Overview of Cloud IoT Systems 252
10.1.2 Role of Edge Computing in IoT 253
10.1.3 Importance of Intrusion Detection 253
10.1.4 Federated Learning: A Decentralized Approach 254
10.2 Background 255
10.2.1 Related Work 255
10.2.1.1 Signature-Based Detection 255
10.2.1.2 Anomaly-Based Detection 256
10.2.1.3 Rule-Based Detection 256
10.2.2 Limitations of Centralized Intrusion Detection in IoT 256
10.2.3 Federated Learning for Security Applications 257
10.2.3.1 Federated Learning: Benefits for IoT Intrusion Detection 257
10.2.3.2 Challenges of Federated Learning in IoT Security 258
10.2.4 Comparative Analysis of Federated Learning and Traditional Machine Learning in Security 258
10.3 Federated Learning in Edge Computing for Intrusion Detection 259
10.3.1 Overview of Federated Learning 259
10.3.2 Architecture of Federated Learning for Edge Computing 259
10.3.3 Federated Learning Workflow for Intrusion Detection 260
10.4 Challenges and Solutions 260
10.4.1 Data Privacy and Security 260
10.4.2 Communication Overhead and Bandwidth Efficiency 261
10.4.3 Model Training Efficiency and Accuracy 263
10.4.4 Scalability in Large-Scale IoT Networks 264
10.5 Proposed Intrusion Detection Framework Using Federated Learning 266
10.5.1 Framework Design and Architecture 266
10.5.2 Model Selection and Training Processes 266
10.5.3 Model Synchronization and Data Combination 267
10.5.4 Federated Intrusion Detection Edge to Cloud Data Flow for Enhanced Security 268
10.6 Implementation and Experimentation 269
10.6.1 Experimental Setup 269
10.6.2 Data Collection and Preprocessing 270
10.6.3 Model Training and Evaluation Metrics 270
10.6.4 Performance Evaluation and Findings 271
10.7 Case Study: Real World Application of Federated Intrusion Detection 272
10.7.1 Case Study Background and Objectives 272
10.7.1.1 Case Study: Enhancing Cybersecurity in Financial Sector with Federated Intrusion Detection 273
10.7.1.2 Case Study: Securing the Smart Grid with Federated Intrusion Detection 273
10.7.2 Implementation Details 274
10.8 Discussion 275
10.8.1 Enhanced Privacy 276
10.8.2 Improved Security 276
10.8.3 Overcoming IoT-Specific Challenges 276
10.8.4 Special Applications of Security in IoT 277
10.8.5 Challenges and Considerations 277
10.9 Future Directions 277
10.9.1 Advanced Federated Learning Techniques for IoT Security 277
10.9.2 Integrating Blockchain for Decentralized Authentication 278
10.9.3 AI in Anomaly Detection 279
10.10 Conclusion 280
References 280
11 Behavioral Profiling for Dynamic Anomaly Detection in Cloud-IoT Networks 283
Triveni Lal Pal and Manoj Kumar Pandey
11.1 Introduction 284
11.1.1 Real Motivation 285
11.1.2 Various Challenges in Securing Cloud-IoT Networks 286
11.1.3 Objectives and Scope of Behavioral Profiling 287
11.1.4 Organization of the Chapter 287
11.2 Cloud IoT Architecture 288
11.3 Literature Study 288
11.3.1 Anomaly Detection Techniques 289
11.3.2 Anomaly Detection in Cloud-IoT Network 292
11.3.3 Machine Learning Based Anomaly Detection 293
11.4 Emerging Trends and Opportunities 294
11.5 Conclusion and Future Direction 296
References 298
12 Immunity against Intrusion: Introducing an Agent-Based Blockchain Mechanism in Cloud IoT Environment 301
Amitabha Mandal and Pramit Ghosh
12.1 Introduction 302
12.1.1 Evolution of Digital System 302
12.1.2 Distributed Sensor Environment 303
12.1.3 Intrusion and Intrusion Detection 305
12.1.4 Internet of Things (IoT) 305
12.1.5 Cloud IoT 309
12.1.6 Blockchain 310
12.2 Contribution of the Authors 311
12.3 Proposed Agent-Based Blockchain Mechanism in Cloud IoT [ABBM Cloud IoT] 311
12.3.1 Proposed Scheme 311
12.3.2 Phase I: Device Registration 313
12.3.3 Phase II: Authentication with Key Management 314
12.3.4 Incorporating Blockchain in Key Management 318
12.4 Results and Discussion 318
12.4.1 Security Analysis 318
12.4.2 Overhead Metrics 320
12.4.2.1 Computation Cost 320
12.4.2.2 Communication Cost 322
12.4.2.3 Storage Cost 323
12.4.3 Blockchain Efficiency 324
12.4.3.1 Transaction Handling 325
12.4.3.2 Block Preparation Time 325
12.4.4 Summary of Results 327
12.5 Conclusion 327
References 328
13 Designing a Hybrid Intrusion Detection System for Wireless Acoustic Sensor Networks: Enhancing Security During Audio Transmission 331
Utpal Ghosh and Uttam Kr. Mondal
13.1 Introduction 332
13.2 Background 333
13.3 Proposed Hybrid IDS Architecture 335
13.3.1 Data Collection 335
13.3.2 Data Preprocessing 336
13.3.3 Signature-Based Detection 337
13.3.4 Anomaly-Based Detection 338
13.3.5 Machine Learning-Based Detection 339
13.3.6 Alert Generation 339
13.3.7 Incident Response 339
13.4 Experimental Setup 340
13.4.1 Simulation Environment 340
13.4.2 Network Topology 341
13.4.3 Audio Signal Characteristics 341
13.4.4 Hybrid Intrusion Detection System (HIDS) Configuration 341
13.4.5 Attack Scenarios 341
13.4.5.1 Scenario 1 341
13.4.5.2 Scenario 2 341
13.4.5.3 Scenario 3 341
13.4.6 Performance Metrics 341
13.4.7 Simulation Duration 342
13.4.8 Datasets 342
13.4.9 Training 342
13.5 Results Analysis and Performance Evaluation 342
13.5.1 Experimental Results 343
13.5.2 Comparative Performance Analysis 345
13.6 Conclusions and Future Scope 350
References 350
Index 353
