Details

Intelligent Network Management and Control

Intelligent Security, Multi-criteria Optimization, Cloud Computing, Internet of Vehicles, Intelligent Radio
1. Aufl.

von: Badr Benmammar
139,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	12.03.2021
ISBN/EAN:	9781119817833
Sprache:	englisch
Anzahl Seiten:	304

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Titelbeschreibung

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 SDN (software-defined networking), optimization of networks using artificial intelligence techniques and multi-criteria optimization methods for selecting networks in a heterogeneous environment. This book also focuses on selecting cloud computing services, intelligent unloading of calculations in the context of mobile cloud computing, intelligent resource management in a smart grid-cloud system for better energy efficiency, new architectures for the Internet of Vehicles (IoV), the application of artificial intelligence in cognitive radio networks and intelligent radio input to meet the on-road communication needs of autonomous vehicles.

Inhaltsverzeichnis

Introduction xiii Badr BENMAMMAR Part 1. AI and Network Security 1 Chapter 1. Intelligent Security of Computer Networks 3 Abderrazaq SEMMOUD and Badr BENMAMMAR 1.1. Introduction 3 1.2. AI in the service of cybersecurity 5 1.3. AI applied to intrusion detection 8 1.3.1. Techniques based on decision trees 9 1.3.2. Techniques based on data exploration 9 1.3.3. Rule-based techniques 10 1.3.4. Machine learning-based techniques 11 1.3.5. Clustering techniques 13 1.3.6. Hybrid techniques 14 1.4. AI misuse 15 1.4.1. Extension of existing threats 16 1.4.2. Introduction of new threats 16 1.4.3. Modification of the typical threat character 17 1.5. Conclusion 17 1.6. References 18 Chapter 2. An Intelligent Control Plane for Security Services Deployment in SDN-based Networks 25 Maïssa MBAYE, Omessaad HAMDI and Francine KRIEF 2.1. Introduction 25 2.2. Software-defined networking 27 2.2.1. General architecture 27 2.2.2. Logical distribution of SDN control 29 2.3. Security in SDN-based networks 32 2.3.1. Attack surfaces 33 2.3.2. Example of security services deployment in SDN-based networks: IPSec service 34 2.4. Intelligence in SDN-based networks 40 2.4.1. Knowledge plane 41 2.4.2. Knowledge-defined networking 41 2.4.3. Intelligence-defined networks 42 2.5. AI contribution to security 43 2.5.1. ML techniques 43 2.5.2. Contribution of AI to security service: intrusion detection 47 2.6. AI contribution to security in SDN-based networks 48 2.7. Deployment of an intrusion prevention service 49 2.7.1. Attack signature learning as cloud service 50 2.7.2. Deployment of an intrusion prevention service in SDN-based networks 52 2.8. Stakes 55 2.9. Conclusion 56 2.10. References 56 Part 2. AI and Network Optimization 63 Chapter 3. Network Optimization using Artificial Intelligence Techniques 65 Asma AMRAOUI and Badr BENMAMMAR 3.1. Introduction 65 3.2. Artificial intelligence 66 3.2.1. Definition 66 3.2.2. AI techniques 67 3.3. Network optimization 73 3.3.1. AI and optimization of network performances 73 3.3.2. AI and QoS optimization 74 3.3.3. AI and security 75 3.3.4. AI and energy consumption 77 3.4. Network application of AI 77 3.4.1. ESs and networks 77 3.4.2. CBR and telecommunications networks 79 3.4.3. Automated learning and telecommunications networks 79 3.4.4. Big data and telecommunications networks 80 3.4.5. MASs and telecommunications networks 82 3.4.6. IoT and networks 84 3.5. Conclusion 85 3.6. References 85 Chapter 4. Multicriteria Optimization Methods for Network Selection in a Heterogeneous Environment 89 Fayssal BENDAOUD 4.1. Introduction 89 4.2. Multicriteria optimization and network selection 91 4.2.1. Network selection process 92 4.2.2. Multicriteria optimization methods for network selection 94 4.3. “Modified-SAW” for network selection in a heterogeneous environment 99 4.3.1. “Modified-SAW” proposed method 100 4.3.2. Performance evaluation 104 4.4. Conclusion 113 4.5. References 113 Part 3. AI and the Cloud Approach 117 Chapter 5. Selection of Cloud Computing Services: Contribution of Intelligent Methods 119 Ahmed Khalid Yassine SETTOUTI 5.1. Introduction 119 5.2. Scientific and technical prerequisites 120 5.2.1. Cloud computing 120 5.2.2. Artificial intelligence 126 5.3. Similar works 129 5.4. Surveyed works 131 5.4.1. Machine learning 131 5.4.2. Heuristics 133 5.4.3. Intelligent multiagent systems 135 5.4.4. Game theory 137 5.5. Conclusion 140 5.6. References 140 Chapter 6. Intelligent Computation Offloading in the Context of Mobile Cloud Computing 145 Zeinab MOVAHEDI 6.1. Introduction 145 6.2. Basic definitions 147 6.2.1. Fine-grain offloading 147 6.2.2. Coarse-grain offloading 149 6.3. MCC architecture 151 6.3.1. Generic architecture of MCC 151 6.3.2. C-RAN-based architecture 154 6.4. Offloading decision 154 6.4.1. Positioning of the offloading decision middleware 155 6.4.2. General formulation 156 6.4.3. Modeling of offloading cost 158 6.5. AI-based solutions 161 6.5.1. Branch and bound algorithm 161 6.5.2. Bio-inspired metaheuristics algorithms 164 6.5.3. Ethology-based metaheuristics algorithms 165 6.6. Conclusion 165 6.7. References 166 Part 4. AI and New Communication Architectures 169 Chapter 7. Intelligent Management of Resources in a Smart Grid-Cloud for Better Energy Efficiency 171 Mohammed Anis BENBLIDIA, Leila MERGHEM-BOULAHIA, Moez ESSEGHIR and Bouziane BRIK 7.1. Introduction 171 7.2. Smart grid and cloud data center: fundamental concepts and architecture 172 7.2.1. Network architecture for smart grids 173 7.2.2. Main characteristics of smart grids 174 7.2.3. Interaction of cloud data centers with smart grids 178 7.3. State-of-the-art on the energy efficiency techniques of cloud data centers 180 7.3.1. Energy efficiency techniques of non-IT equipment of a data center 180 7.3.2. Energy efficiency techniques in data center servers 181 7.3.3. Energy efficiency techniques for a set of data centers 182 7.3.4. Discussion 184 7.4. State-of-the-art on the decision-aiding techniques in a smart grid-cloud system 185 7.4.1. Game theory 186 7.4.2. Convex optimization 187 7.4.3. Markov decision process 187 7.4.4. Fuzzy logic 187 7.5. Conclusion 188 7.6. References 189 Chapter 8. Toward New Intelligent Architectures for the Internet of Vehicles 193 Léo MENDIBOURE, Mohamed Aymen CHALOUF and Francine KRIEF 8.1. Introduction 193 8.2. Internet of Vehicles 195 8.2.1. Positioning 195 8.2.2. Characteristics 196 8.2.3. Main applications 197 8.3. IoV architectures proposed in the literature 197 8.3.1. Integration of AI techniques in a layer of the control plane 199 8.3.2. Integration of AI techniques in several layers of the control plane 199 8.3.3. Definition of a KP associated with the control plane 200 8.3.4. Comparison of architectures and positioning 200 8.4. Our proposal of intelligent IoV architecture 201 8.4.1. Presentation 202 8.4.2. A KP for data transportation 203 8.4.3. A KP for IoV architecture management 205 8.4.4. A KP for securing IoV architecture 207 8.5. Stakes 209 8.5.1. Security and private life 210 8.5.2. Swarm learning 210 8.5.3. Complexity of computing methods 210 8.5.4. Vehicle flow motion 211 8.6. Conclusion 211 8.7. References 212 Part 5. Intelligent Radio Communications 217 Chapter 9. Artificial Intelligence Application to Cognitive Radio Networks 219 Badr BENMAMMAR and Asma AMRAOUI 9.1. Introduction 219 9.2. Cognitive radio 222 9.2.1. Cognition cycle 222 9.2.2. CR tasks and corresponding challenges 223 9.3. Application of AI in CR 223 9.3.1. Metaheuristics 223 9.3.2. Fuzzy logic 229 9.3.3. Game theory 230 9.3.4. Neural networks 231 9.3.5. Markov models 231 9.3.6. Support vector machines 232 9.3.7. Case-based reasoning 233 9.3.8. Decision trees 233 9.3.9. Bayesian networks 234 9.3.10. MASs and RL 234 9.4. Categorization and use of techniques in CR 236 9.5. Conclusion 237 9.6. References 237 Chapter 10. Cognitive Radio Contribution to Meeting Vehicular Communication Needs of Autonomous Vehicles 245 Francine KRIEF, Hasnaâ ANISS, Marion BERBINEAU and Killian LE PAGE 10.1. Introduction 245 10.2. Autonomous vehicles 246 10.2.1. Automation levels 246 10.2.2. The main components 247 10.3. Connected vehicle 251 10.3.1. Road safety applications 251 10.3.2. Entertainment applications 252 10.4. Communication architectures 253 10.4.1. ITS-G5 256 10.4.2. LTE-V2X 257 10.4.3. Hybrid communication 258 10.5. Contribution of CR to vehicular networks 258 10.5.1. Cognitive radio 259 10.5.2. CR-VANET 260 10.6. SERENA project: self-adaptive selection of radio access technologies using CR 264 10.6.1. Presentation and positioning 265 10.6.2. General architecture being considered 266 10.6.3. The main stakes 269 10.7. Conclusion 270 10.8. References 270 List of Authors 275 Index 277

Autorenportrait

Badr Benmammar is currently a Professor in the Computer Science department at the Abou Bakr Belkaïd University of Tlemcen, Algeria, having received his PhD in Computer Science from Bordeaux 1 University, France. He is the author of several books, including Radio Resource Allocation and Dynamic Spectrum Access (ISTE-Wiley), and his work has led to many journal publications.