Оглавление
Badr Benmammar. Intelligent Network Management and Control
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Intelligent Network Management and Control. Intelligent Security, Multi-criteria Optimization, Cloud Computing, Internet of Vehicles, Intelligent Radio
Introduction
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
1.3.2.1. Fuzzy logic
1.3.2.2. Genetic algorithms
1.3.3.Rule-based techniques
1.3.4.Machine learning-based techniques
1.3.4.1. Artificial neural networks
1.3.4.2. Bayesian networks
1.3.4.3. Markov chains
1.3.4.4. Support-vector machines
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
2.3.2.1. General architecture
2.3.2.2. Deployment performance evaluation
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
2.5.1.1. Supervised learning techniques
2.5.1.2. Unsupervised learning 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
3. Network Optimization using Artificial Intelligence Techniques
3.1. Introduction
3.2. Artificial intelligence. 3.2.1.Definition
3.2.2.AI techniques. 3.2.2.1. Expert systems
3.2.2.2. Case-based reasoning
3.2.2.3. Machine learning
3.2.2.4. Neural networks
3.2.2.5. Multiagent systems
3.2.2.6. Internet of Things
3.2.2.7. Cloud computing
3.2.2.8. Big data
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. 3.4.1.1. ES for machine maintenance
3.4.1.2. ES for the diagnosis of a multiplexer network
3.4.2.CBR and telecommunications networks
3.4.3.Automated learning and telecommunications networks
3.4.4.Big data and telecommunications networks. 3.4.4.1. Big data and customer service improvement
3.4.4.2. Big data and security
3.4.5.MASs and telecommunications networks
3.4.5.1. MAS and CR
3.4.5.2. MAS and transport 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
4.2.2.1. Simple additive weight
4.2.2.2. Technique for order preference by similarity to ideal solution
4.2.2.3. Weighted product model
4.2.2.4. Analytical hierarchy process and gray relational analysis
4.2.2.5. Discussion
4.3. “Modified-SAW” for network selection in a heterogeneous environment
4.3.1.“Modified-SAW” proposed method
4.3.2.Performance evaluation
4.3.2.1. Simulation 1: availability of all networks
VoIP
Video service
Best effort services
4.3.2.2. Simulation 2: rank reversal
4.3.2.3. Discussion
4.4. Conclusion
4.5. References
5. Selection of Cloud Computing Services: Contribution of Intelligent Methods
5.1. Introduction
5.2. Scientific and technical prerequisites
5.2.1.Cloud computing
5.2.1.1. Cloud computing characteristics
5.2.1.1.1. On-demand service
5.2.1.1.2. Access via Internet
5.2.1.1.3. Resource grouping
5.2.1.1.4. Rapid elasticity
5.2.1.1.5. Measured service
5.2.1.2. Deployment models
5.2.1.2.1. Public cloud
5.2.1.2.2. Private cloud
5.2.1.2.3. Community cloud
5.2.1.2.4. Hybrid model
5.2.1.3. Levels of Cloud Computing services. 5.2.1.3.1. Infrastructure-as-a-service
5.2.1.3.2. Software-as-a-service
5.2.1.3.3. Platform-as-a-service
5.2.2.Artificial intelligence
5.2.2.1. Subjects
5.2.2.2. Approaches
5.2.2.3. Tools
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
6.3.1.1. Mobile device
6.3.1.2. Access network
6.3.1.3. Backhaul and backbone networks
6.3.1.4. Cloud
6.3.1.5. Offloading decision middleware
6.3.2.C-RAN-based architecture
6.4. Offloading decision
6.4.1. Positioning of the offloading decision middleware
6.4.1.1. Middleware embedded in the mobile device
6.4.1.2. Outsourced middleware
6.4.2.General formulation
6.4.2.1. Offloading decision variables
6.4.2.2. Objective function of the offloading problem
6.4.3.Modeling of offloading cost. 6.4.3.1. Fine-grain offloading cost
6.4.3.2. Coarse-grain 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
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
7.2.1.1. Home area network
7.2.1.2. Neighborhood area network
7.2.1.3. Wide area network
7.2.2.Main characteristics of smart grids
7.2.2.1. Bidirectional communication
7.2.2.2. Distributed energy resources
7.2.2.3. Management of the electrical system equilibrium through DR mechanisms
7.2.2.4. Smart pricing
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
7.3.3.1. Smart grid-cloud architecture
7.3.3.2. Smart micro-grid-cloud architecture
7.3.4.Discussion
7.4. State-of-the-art on the decision-aiding techniques in a smart grid-cloud 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
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
9.3.1.1. Firefly algorithm
9.3.1.2. Cuckoo search
9.3.1.3. Bee colony algorithm
9.3.1.4. Genetic algorithms
9.3.1.5. Gravitational search algorithm
9.3.1.6. Particle swarm optimization
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
Support access control (MAC)
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
10.5.2.1. Detection of spectrum sensing
10.5.2.2. Spectrum and QoS management
10.5.2.3. Network
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
10.6.2.1. Cognition loop and cognitive manager
Specification of the cognition loop
Cognitive manager specification
10.6.2.2. Network function virtualization
10.6.3.The main stakes
10.7. Conclusion
10.8. References
List of Authors
Index
A, B
C, D
E, F
G, H
I, K
L, M
N, O
P, Q
R, S
T, V
W, Z
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