Intelligent Security Management and Control in the IoT

Description

The Internet of Things (IoT) has contributed greatly to the growth of data traffic on the Internet. Access technologies and object constraints associated with the IoT can cause performance and security problems. This relates to important challenges such as the control of radio communications and network access, the management of service quality and energy consumption, and the implementation of security mechanisms dedicated to the IoT. In response to these issues, this book presents new solutions for the management and control of performance and security in the IoT. The originality of these proposals lies mainly in the use of intelligent techniques. This notion of intelligence allows, among other things, the support of object heterogeneity and limited capacities as well as the vast dynamics characterizing the IoT. Cover Half-Title Page Title Page Copyright Page Contents 1. Multicriteria Selection of Transmission Parameters in the IoT 1.2. Changing access network in the IoT 1.3. Spectrum handoff in the IoT 1.4. Multicriteria decision-making module for an effective spectrum handoff in the IoT 1.4.1. General architecture 1.4.2. Decision-making flowchart 1.4.3. Performances evaluation 1.5. Conclusion 1.6. References 2. Using Reinforcement Learning to Manage Massive Access in NB-IoT Networks 2.1. Introduction 2.2. Fundamentals of the NB-IoT standard 2.2.1. Deployment and instances of use 2.2.2. Transmission principles 2.2.3. Radio resource random access procedure 2.3. State of the art 2.4. Model for accessing IoT terminals 2.5. Access controller for IoT terminals based on reinforcement learning 2.5.1. Formulating the problem 2.5.2. Regulation system for arrivals 2.6. Performance evaluation 2.7. Conclusion 2.8. References 3. Optimizing Performances in the IoT: An Approach Based on Intelligent Radio 3.1. Introduction 3.2. Internet of Things (IoT) 3.2.1. Definition of the IoT 3.2.2. Applications of the IoT 3.2.3. IoT challenges 3.2.4. Enabling technologies in the IoT 3.3. Intelligent radio 3.3.1. Definition of intelligent radio 3.3.2. Motivations for using intelligent radio in the IoT 3.3.3. Challenges in using intelligent radio in the IoT 3.4. Conclusion 3.5. References 4. Optimizing the Energy Consumption of IoT Devices 4.1. Introduction 4.2. Energy optimization 4.2.1. Definitions 4.3. Optimization techniques for energy consumption 4.3.1. The A* algorithm 4.3.2. Fuzzy logic 4.4. Energy optimization in the IoT 4.4.1. Characteristics of the IoT 4.4.2. Challenges in energy optimization 4.4.3. Research on energy optimization in the IoT 4.5. Autonomous energy optimization framework in the IoT 4.5.1. Autonomous computing 4.5.2. Framework specification 4.6. Proposition of a self-optimization method for energy consumption in the IoT 4.6.1. Fuzzy logic model 4.6.2. Decision-making algorithm 4.6.3. Evaluating energy self-optimization in the IoT 4.7. Conclusion 4.8. References 5. Toward Intelligent Management of Service Quality in the IoT: The Case of a Low Rate WPAN 5.1. Introduction 5.2. Quick overview of the IoT 5.2.2. Technologies for the IoT 5.2.3. IoT and quality of service 5.3. IEEE 802.15.4 TSCH approach 5.4. Transmission scheduling 5.4.1. General considerations 5.4.2. Scheduling in the literature 5.5. Routing and RPL 5.5.1. Routing 5.5.2. RPL 5.5.3. Multipath 5.6. Combined approach based on 802.15.4 TSCH and multipath RPL 5.6.1. Automatic Repeat reQuest 5.6.2. Replication and Elimination 5.6.3. Overhearing 5.7. Conclusion 5.8. References 6. Adapting Quality of Service of Energy-Harvesting IoT Devices 6.1. Toward the energy autonomy of sensor networks 6.1.1. Energy harvesting and management 6.1.2. State-of-the-art energy managers 6.2. Fuzzyman: use of fuzzy logic 6.2.1. Design of Fuzzyman 6.2.2. Evaluating Fuzzyman 6.2.3. Conclusion 6.3. RLMan: using reinforcement learning 6.3.1. Formulating the problem of managing the harvested energy 6.3.2. RLMan algorithm 6.3.3. Evaluation of RLMan 6.3.4. Conclusion 6.4. Toward energy autonomous LoRa nodes 6.4.1. Multisource energy-harvesting architecture 6.4.2. Applying energy management to LoRa nodes 6.5. Conclusion 6.6. References 7. Adapting Access Control for IoT Security 7.1. Introduction 7.2. Defining security services in the IoT 7.2.1. Identification and authentication in the IoT 7.2.2. Access control in the IoT 7.2.3. Confidentiality in the IoT 7.2.4. Integrity in the IoT 7.2.5. Non-repudiation in the IoT 7.2.6. Availability in the IoT 7.3. Access control technologies 7.4. Access control in the IoT 7.4.1. Research on the extension of access control models for the IoT 7.4.2. Research on adapting access control systems and technologies for 7.5. Access control framework in the IoT 7.5.1. IoT architecture 7.5.2. IoT-MAAC access control specification 7.6. Conclusion 7.7. References 8. The Contributions of Biometrics and Artificial Intelligence in Securing the IoT 8.1. Introduction 8.2. Security and privacy in the IoT 8.3. Authentication based on biometrics 8.3.1. Biometrics 8.3.2. Biometric techniques 8.3.3. The different properties of biometrics 8.3.4. Operating a biometric system 8.3.5. System performances 8.4. Multifactor authentication techniques based on biometrics 8.4.1. Multifactor authentication 8.4.2. Examples of multifactor authentication approaches for securing 8.4.3. Presentation of the approach of Sammoud et al. (2020c) 8.5. Authentication techniques based on biometrics and machine learning 8.5.1. Machine learning algorithms 8.5.2. Examples of authentication approaches based on biometrics and 8.5.3. Authentication approaches based on ECG and machine learning 8.6. Challenges and limits 8.6.1. Quality of biometric data 8.6.2. Non-revocability of biometric data 8.6.3. Security of biometric systems 8.7. Conclusion 8.8. References 9. Dynamic Identity and Access Management in the IoT: Blockchain-based Approach 9.1. Introduction 9.2. Context 9.2.1. Intelligent identity and access management 9.2.2. Blockchain 9.3. Blockchain for intelligent identity and access management 9.3.1. A new architecture integrating blockchain 9.3.2. The different benefits 9.4. Challenges 9.4.1. Scaling up 9.4.2. Blockchain security 9.4.3. Energy consumption 9.4.4. Definition of consensus algorithms based on artificial 9.5. Conclusion 9.6. References 10. Adapting the Security Level of IoT Applications 10.1. Introduction 10.2. Definitions and characteristics 10.2.1. Definitions 10.2.2. Characteristics 10.3. IoT applications 10.4. IoT architectures 10.5. Security, trust and privacy protection in IoT applications 10.5.1. General remarks 10.5.2. Security services 10.5.3. Communication security 10.5.4. Trust 10.5.5. Privacy 10.6. Adapting the security level in the IoT 10.6.1. Context-awareness 10.6.2. Context-aware security 10.6.3. Context-aware security architecture and privacy protection 10.7. Conclusion 10.8. References 11. Moving Target Defense Techniques for the IoT 11.1. Introduction 11.2. Background 11.2.1. Brief chronology of Moving Target Defense 11.2.2. Fundamental technical and taxonomic principles of MTD 11.3. Related works 11.3.1. Surveys on MTD techniques 11.3.2. Frameworks for IoT systems linked to the concept of MTD 11.4. LMTD for the IoT: a qualitative survey 11.4.1. Data: MTD mechanism against side-channel channel attacks 11.4.2. Software 11.4.3. Runtime environment 11.4.4. Platform: diversifying by reconfiguring the IoT node firmware 11.4.5. Networks 11.4.6. Section summary 11.5. Network components in the IoT: a vast domain for MTD 11.5.1. Physical layer 11.5.2. Link layer 11.5.3. OSI network layer 11.5.4. Transport layer 11.5.5. Application layer 11.5.6. Section summary 11.6. An MTD framework for the IoT 11.6.1. Proposition: components 11.6.2. Instantiation: UDP port hopping 11.7. Discussion and avenues for future research 11.8. Conclusion 11.9. References List of Authors Index