Intelligent network management and control : intelligent security, multi-criteria optimization, cloud computing, Internet of Vehicles, intelligent radio

Intelligent network management and control intelligent security, multi-criteria optimization, cloud computing, Internet of Vehicles, intelligent radio

Detalles Bibliográficos
Otros Autores: Benmammar, Badr, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: London : Hoboken, NJ : ISTE 2020.
Materias:
Computer networks > Security measures.
Artificial intelligence.
Internet of things.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631468906719
Tabla de Contenidos:
  • 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.

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