Оглавление
Mohamed-Aymen Chalouf. Intelligent Security Management and Control in the IoT
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Intelligent Security Management and Control in the IoT
1. Multicriteria Selection of Transmission Parameters in the IoT
1.1. Introduction
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
1.4.1.1. Detection for an intelligent radio module
1.4.1.2. Prediction module
1.4.1.2.1. Probability of channel availability at period t + 1
1.4.1.2.2. Average channel availability time
1.4.1.3. Object characteristics
1.4.1.4. Network monitoring module
1.4.1.5. Database
1.4.1.6. Multicriteria decision-making module
1.4.1.7. Module for running vertical handover/spectrum handoff
1.4.2.Decision-making flowchart
1.4.2.1. Stage 1: selecting available access networks/CR channels
1.4.2.2. Stage 2: classifying channels depending on the energy cost of the transmission models
1.4.2.3. Stage 3: calculation of the QoS score of the transmission models
1.4.2.4. Stage 4: attribution of weight and calculating the final score
1.4.3.Performances evaluation
1.4.3.1. Scenario 1: network selection – the case of V2I communications
1.4.3.2. Scenario 2: selecting the radio channel – the case of CR-VANET
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
2.2.2.1. Modes of deployment
2.2.2.2. Physical layer
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
3.2.4.1. Cloud/Fog/Edge Computing
3.2.4.2. Network of wireless sensors
3.2.4.3. 5G
3.2.4.4. LPWA (Low Power Wide Area) technologies for the mobile IoT
3.2.4.5. Big Data
3.2.4.6. Machine learning
3.2.4.7. Communication protocols
3.3. Intelligent radio. 3.3.1.Definition of intelligent radio
3.3.1.1. Spectrum detection
3.3.1.2. Spectrum decision
3.3.1.3. Spectrum analysis
3.3.1.4. Spectrum mobility
3.3.2.Motivations for using intelligent radio in the IoT
3.3.3.Challenges in using intelligent radio in the IoT
3.3.3.1. Energy recovery
3.3.3.2. Spectrum allocation
3.3.3.3. Spectrum detection
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. 4.2.1.1. Energy optimization
4.2.1.2. System lifespan
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. 5.2.1.The micro-IPv6 stack
5.2.2.Technologies for the IoT
5.2.2.1. Bluetooth Low Energy
5.2.2.2. WiFi HaLow
5.2.2.3. WirelessHART
5.2.2.4. ISA100
5.2.2.5. IEEE 802.15.4 standard
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
5.4.2.1. TASA
5.4.2.2. DeTAS
5.4.2.3. Orchestra
5.4.2.4. LOST
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
6.1.1.1. Challenges for designing EMs
6.1.2.State-of-the-art energy managers
6.1.2.1. Energy managers with prediction
6.1.2.2. Energy managers without prediction
6.2. Fuzzyman: use of fuzzy logic. 6.2.1.Design of Fuzzyman. 6.2.1.1. Fuzzyman architecture
6.2.1.2. Fuzzification of the controller’s inputs
6.2.1.2.1. Fuzzification of recovered energy
6.2.1.2.2. Fuzzification of residual energy
6.2.1.3. Inference motor
6.2.1.4. Defuzzing the energy budget
6.2.2.Evaluating Fuzzyman. 6.2.2.1. Simulation environment
6.2.2.2. Energy traces
6.2.2.3. Simulation results
6.2.3.Conclusion
6.3. RLMan: using reinforcement learning. 6.3.1.Formulating the problem of managing the harvested energy
6.3.1.1. The reward function R
6.3.1.2. The depreciation factor γ
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 the IoT
7.5. Access control framework in the IoT
7.5.1.IoT architecture
7.5.2.IoT-MAAC access control specification
7.5.2.1. Access control attributes
7.5.2.2. Access control architecture based on XACML/SAML
7.5.2.3. Evaluating object trust (TSK fuzzy logic model)
7.5.2.4. Decision-making algorithm
7.5.2.5. Usage scenario
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 the IoT
8.4.3.Presentation of the approach of Sammoud et al. (2020c)
8.4.3.1. Notation
8.4.3.2. Setup phase
8.4.3.3. Healthcare professional registration phase
8.4.3.4. Login phase
8.4.3.5. Authentication phase
8.4.3.6. Password change phase
8.4.3.7. Discussion
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 machine learning
8.5.3.Authentication approaches based on ECG and machine learning. 8.5.3.1. Overall schema for authentication by ECG
8.5.3.2. Authentication approaches based on ECG
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
9.3.2.1. Load distribution
9.3.2.2. Establishing trust
9.3.2.3. Anonymizing users
9.3.2.4. The advent of new intelligent approaches
9.3.2.5. Explainability of the decision process
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 intelligence
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
10.2.2.1. Sensitive data
10.2.2.2. Heterogeneity
10.2.2.3. Restricted resources
10.2.2.4. Dynamism
10.2.2.5. Intelligence
10.2.2.6. Real time
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
10.5.2.1. Authentication and access control. 10.5.2.1.1. Authentication
10.5.2.1.2. Access control
10.5.3.Communication security
10.5.3.1. Integrity. 10.5.3.1.1. Definition
10.5.3.1.2. Research
10.5.3.2. Confidentiality. 10.5.3.2.1. Definition
10.5.3.2.2. Research
10.5.4.Trust. 10.5.4.1. Definition
10.5.4.2. Research
10.5.5.Privacy. 10.5.5.1. Definition
10.5.5.2. Research
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 designed using the “as a service” approach
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 based on renegotiating cryptographic keys
11.4.2. Software. 11.4.2.1. Diversifying by reconfiguring cryptographic protocols of nodes
11.4.2.2. Partitioning and diversification of binary code
11.4.2.3. μArmor-μScramble: embedded binary security
11.4.3.Runtime environment. 11.4.3.1. Diversifying the internal interfaces in operating systems
11.4.3.2. μArmor-μSSP: embedded binary security
11.4.4.Platform: diversifying by reconfiguring the IoT node firmware
11.4.5.Networks. 11.4.5.1. μMT6D : randomization of IPv6 addresses
11.4.5.2. SARCAST: randomization of IPv6 multicast addresses
11.4.5.3. Reconfiguring the topology of IoT Software-Defined Networks (SDN IoT)
11.4.5.4. “Honeypot” MTD for cell-phone assisted IoT systems
11.4.5.5. AShA: randomization of MAC-IPv6 addresses
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 MTDframeworkfor 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. A
B, C
D, E
F
I
L
M
N
O, P
Q, R
S
T, V
W, X
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