Intelligent Network Managment and Control

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