Intelligent Network Management and Control Intelligent Security, Multi-Criteria Optimization, Cloud Computing, Internet of Vehicles, Intelligent Radio

The management and control of networks can no longer be envisaged without the introduction of artificial intelligence at all stages. Intelligent Network Management and Control deals with topical issues related mainly to intelligent security of computer networks, deployment of security services in SD...

Full description

Saved in:
Bibliographic Details
Main Author: Benmammar, Badr
Format: eBook
Language:English
Published: Newark John Wiley & Sons, Incorporated 2021
Wiley-Blackwell
Edition:1
Subjects:
Artificial intelligence
Computer networks-Security measures
Internet of things
ISBN:178945008X, 9781789450088
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Table of Contents:
  • Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Introduction -- PART 1: AI and Network Security -- 1 Intelligent Security of Computer Networks -- 1.1. Introduction -- 1.2. AI in the service of cybersecurity -- 1.3. AI applied to intrusion detection -- 1.3.1. Techniques based on decision trees -- 1.3.2. Techniques based on data exploration -- 1.3.3. Rule-based techniques -- 1.3.4. Machine learning-based techniques -- 1.3.5. Clustering techniques -- 1.3.6. Hybrid techniques -- 1.4. AI misuse -- 1.4.1. Extension of existing threats -- 1.4.2. Introduction of new threats -- 1.4.3. Modification of the typical threat character -- 1.5. Conclusion -- 1.6. References -- 2 An Intelligent Control Plane for Security Services Deployment in SDN-based Networks -- 2.1. Introduction -- 2.2. Software-defined networking -- 2.2.1. General architecture -- 2.2.2. Logical distribution of SDN control -- 2.3. Security in SDN-based networks -- 2.3.1. Attack surfaces -- 2.3.2. Example of security services deployment in SDN-based networks: IPSec service -- 2.4. Intelligence in SDN-based networks -- 2.4.1. Knowledge plane -- 2.4.2. Knowledge-defined networking -- 2.4.3. Intelligence-defined networks -- 2.5. AI contribution to security -- 2.5.1. ML techniques -- 2.5.2. Contribution of AI to security service: intrusion detection -- 2.6. AI contribution to security in SDN-based networks -- 2.7. Deployment of an intrusion prevention service -- 2.7.1. Attack signature learning as cloud service -- 2.7.2. Deployment of an intrusion prevention service in SDN-based networks -- 2.8. Stakes -- 2.9. Conclusion -- 2.10. References -- PART 2: AI and Network Optimization -- 3 Network Optimization using Artificial Intelligence Techniques -- 3.1. Introduction -- 3.2. Artificial intelligence -- 3.2.1. Definition -- 3.2.2. AI techniques -- 3.3. Network optimization
  • 3.3.1. AI and optimization of network performances -- 3.3.2. AI and QoS optimization -- 3.3.3. AI and security -- 3.3.4. AI and energy consumption -- 3.4. Network application of AI -- 3.4.1. ESs and networks -- 3.4.2. CBR and telecommunications networks -- 3.4.3. Automated learning and telecommunications networks -- 3.4.4. Big data and telecommunications networks -- 3.4.5. MASs and telecommunications networks -- 3.4.6. IoT and networks -- 3.5. Conclusion -- 3.6. References -- 4 Multicriteria Optimization Methods for Network Selection in a Heterogeneous Environment -- 4.1. Introduction -- 4.2. Multicriteria optimization and network selection -- 4.2.1. Network selection process -- 4.2.2. Multicriteria optimization methods for network selection -- 4.3. "Modified-SAW" for network selection in a heterogeneous environment -- 4.3.1. "Modified-SAW" proposed method -- 4.3.2. Performance evaluation -- 4.4. Conclusion -- 4.5. References -- PART 3: AI and the Cloud Approach -- 5 Selection of Cloud Computing Services: Contribution of Intelligent Methods -- 5.1. Introduction -- 5.2. Scientific and technical prerequisites -- 5.2.1. Cloud computing -- 5.2.2. Artificial intelligence -- 5.3. Similar works -- 5.4. Surveyed works -- 5.4.1. Machine learning -- 5.4.2. Heuristics -- 5.4.3. Intelligent multiagent systems -- 5.4.4. Game theory -- 5.5. Conclusion -- 5.6. References -- 6 Intelligent Computation Offloading in the Context of Mobile Cloud Computing -- 6.1. Introduction -- 6.2. Basic definitions -- 6.2.1. Fine-grain offloading -- 6.2.2. Coarse-grain offloading -- 6.3. MCC architecture -- 6.3.1. Generic architecture of MCC -- 6.3.2. C-RAN-based architecture -- 6.4. Offloading decision -- 6.4.1. Positioning of the offloading decision middleware -- 6.4.2. General formulation -- 6.4.3. Modeling of offloading cost -- 6.5. AI-based solutions
  • 6.5.1. Branch and bound algorithm -- 6.5.2. Bio-inspired metaheuristics algorithms -- 6.5.3. Ethology-based metaheuristics algorithms -- 6.6. Conclusion -- 6.7. References -- PART 4: AI and New Communication Architectures -- 7 Intelligent Management of Resources in a Smart Grid-Cloud for Better Energy Efficiency -- 7.1. Introduction -- 7.2. Smart grid and cloud data center: fundamental concepts and architecture -- 7.2.1. Network architecture for smart grids -- 7.2.2. Main characteristics of smart grids -- 7.2.3. Interaction of cloud data centers with smart grids -- 7.3. State-of-the-art on the energy efficiency techniques of cloud data centers -- 7.3.1. Energy efficiency techniques of non-IT equipment of a data center -- 7.3.2. Energy efficiency techniques in data center servers -- 7.3.3. Energy efficiency techniques for a set of data centers -- 7.3.4. Discussion -- 7.4. State-of-the-art on the decision-aiding techniques in a smart gridcloud system -- 7.4.1. Game theory -- 7.4.2. Convex optimization -- 7.4.3. Markov decision process -- 7.4.4. Fuzzy logic -- 7.5. Conclusion -- 7.6. References -- 8 Toward New Intelligent Architectures for the Internet of Vehicles -- 8.1. Introduction -- 8.2. Internet of Vehicles -- 8.2.1. Positioning -- 8.2.2. Characteristics -- 8.2.3. Main applications -- 8.3. IoV architectures proposed in the literature -- 8.3.1. Integration of AI techniques in a layer of the control plane -- 8.3.2. Integration of AI techniques in several layers of the control plane -- 8.3.3. Definition of a KP associated with the control plane -- 8.3.4. Comparison of architectures and positioning -- 8.4. Our proposal of intelligent IoV architecture -- 8.4.1. Presentation -- 8.4.2. A KP for data transportation -- 8.4.3. A KP for IoV architecture management -- 8.4.4. A KP for securing IoV architecture -- 8.5. Stakes -- 8.5.1. Security and private life
  • 8.5.2. Swarm learning -- 8.5.3. Complexity of computing methods -- 8.5.4. Vehicle flow motion -- 8.6. Conclusion -- 8.7. References -- PART 5: Intelligent Radio Communications -- 9 Artificial Intelligence Application to Cognitive Radio Networks -- 9.1. Introduction -- 9.2. Cognitive radio -- 9.2.1. Cognition cycle -- 9.2.2. CR tasks and corresponding challenges -- 9.3. Application of AI in CR -- 9.3.1. Metaheuristics -- 9.3.2. Fuzzy logic -- 9.3.3. Game theory -- 9.3.4. Neural networks -- 9.3.5. Markov models -- 9.3.6. Support vector machines -- 9.3.7. Case-based reasoning -- 9.3.8. Decision trees -- 9.3.9. Bayesian networks -- 9.3.10. MASs and RL -- 9.4. Categorization and use of techniques in CR -- 9.5. Conclusion -- 9.6. References -- 10 Cognitive Radio Contribution to Meeting Vehicular Communication Needs of Autonomous Vehicles -- 10.1. Introduction -- 10.2. Autonomous vehicles -- 10.2.1. Automation levels -- 10.2.2. The main components -- 10.3. Connected vehicle -- 10.3.1. Road safety applications -- 10.3.2. Entertainment applications -- 10.4. Communication architectures -- 10.4.1. ITS-G5 -- 10.4.2. LTE-V2X -- 10.4.3. Hybrid communication -- 10.5. Contribution of CR to vehicular networks -- 10.5.1. Cognitive radio -- 10.5.2. CR-VANET -- 10.6. SERENA project: self-adaptive selection of radio access technologies using CR -- 10.6.1. Presentation and positioning -- 10.6.2. General architecture being considered -- 10.6.3. The main stakes -- 10.7. Conclusion -- 10.8. References -- List of Authors -- Index -- EULA