1Chapter 1 Artificial Intelligence and Machine Learning Basics
12816.3.1 Spatial GNN Architectures
21.1 Introduction
12916.3.2 Temporal GNN Models
31.2 Definition of AI
13016.4 Applications of Network Embeddings : References
41.3 Definition of Machine Learning
131Chapter 17 Computer Vision for Network Monitoring
51.4 Types of Machine Learning Algorithms
13217.1 Algorithms Analyzing Network Topology Imagery
61.5 Common Machine Learning Algorithms
13317.2 Optical Spectrum Data
71.6 Real-world Applications of AI and Machine Learning: References
13417.3 Visualization of Packet Traces: References
82.1 Why AI for Networks?
13518.1 Text Sources in Networking
92.2 Application Areas of AI in Networking
13618.2 NLP Processing Pipeline
102.3 Benefits and Current Challenges: References
13718.3 Applications in Network Management: References
11Chapter 3 Deep Learning Models
13819.1 Neural Architecture Search Fundamentals: References
123.1 Introduction
13920.1 Network Slicing Overview: 20.1.1 Types of Slices
133.2 What is Deep Learning?
14020.2 Intelligent Slice Creation
143.3 Popularity of Deep Learning
14120.3 Continuous Assurance Management
153.4 Deep Learning Models
14220.4 Intelligent Slice Decommissioning: References
163.5 How Deep Learning works
143Chapter 21 Edge Intelligence and Caching
173.6 Key Capabilities: References
14421.1 Distributed Models Optimizing Caching
18Chapter 4 Deep Learning for Computer Networks
14521.1.1 Caching Optimization Challenges
194.1 Applications of Deep Learning in Computer Networks
14621.1.2 Content Delivery Network Optimization
204.11 Traffic Classification and Forecasting
14721.2 Offloading and Coordination : References
214.12 Anomaly Detection
148Chapter 22 Network Automation and Controllers
224.13 Network Security
14922.1 Integrations with SDN
234.14 Quality of Service Optimizations
15022.2 Integrations with NFV
244.15 Automated Network Management
15122.3 Intent Based Networking: References
254.2 Real World Case Studies : References
15223.1 Traffic Classification
26Chapter 5 Deep Learning for Software Defined Networks
15323.2 Traffic Forecasting
275.1 Introduction to SDN and its Key Properties
15423.3 Network Scheduling : References
285.2 Using Deep Learning for Intelligent SDN Controllers
155Chapter 24 Wired Networks
295.3 Traffic Engineering with Deep Reinforcement Learning
15624.1 Data Centers
305.4 Security Enhancements Through Deep Learning: References
15724.2 Backbone Networks
316.1 Key Requirements and Considerations
158References: References
326.2 Data Collection and Preprocessing
15926.1 Attack Vectors
336.3 Model Architectures and Hyperparameters
16026.2 Defense Strategies: References
346.4 Training, Testing and Deployment Strategies: References
161Chapter 27 Network Explainability
35Chapter 7 AI and DL Tools for Network Analysis
16227.1 Introduction
367.1 TensorFlow
16327.2 Taxonomy of Explanations
377.2 PyTorch : References:
16427.3 Explanation Techniques
38Chapter 8 Libraries in AI and ML
16527.4 Networking Applications: References
398.1 Introduction to Libraries in AI
16628.1 Development Lifecycles
408.2 Functioning of AI Libraries
16728.2 Challenges Bridging Research to Production
418.3 Role in Advancing Machine Learning
168Conclusion: References
428.4 Overview of Popular AI Libraries : References
16929.1 Machine Learning for IoT Devices
43Chapter 9 Supervised Learning in Network Security
17029.2 Federated Learning
449.1 Intrusion Detection Using Supervised Learning
17129.3 Split Learning
459.1.1 Supervised Learning Models for Intrusion detection system (IDS)
17229.4 Model Compression: References
469.1.2 Traffic Feature Extraction
17330.1 Network Data Collection
479.1.3 Challenges for Supervised IDS
17430.2 Synthetic Data Generation
489.2 Classification Algorithms for Network Security
17530.3 Benchmarking Processes: References
499.2.1 Malware Classification
17631.1 Purpose-Built Hardware
509.2.2 Insider Threat Detection
17731.2 TPU, FPGA and ASIC Comparison
519.2.3 DDoS Attack Characterization
17831.3 Infrastructure Integration: References
529.2.4 Encrypted Traffic Analysis : References
179Chapter 32 Continual Learning and Concept Drift
53Chapter 10 Unsupervised Learning in Network Anomaly Detection
18032.1 Retraining Triggers
5410.1 Clustering Algorithms for Anomaly Detection
18132.2 Adaptive Ensemble Weighting
5510.1.1 K-Means Clustering
18232.3 Catastrophic Forgetting Mitigation: References
5610.1.2 Density and Hierarchical Clustering
183Chapter 33 Advanced Network Anomaly Detection
5710.1.3 Subspace and Projected Clustering
18433.1 Unsupervised Models for Novel Threats
5810.1.4 Model-Based Clustering
18533.2 Sequence Models for Attacks
5910.2 Autoencoders and Their Application
18633.3 Flow-based Analysis Models: References:
6010.2.1 Autoencoder Architecture and Training
187Chapter 34 AI For Wireless Network Optimization
6110.2.2 Denoising Autoencoders
18834.1 Client Mobility Prediction
6210.2.3 Variational Autoencoders: References
18934.2 Dynamic Spectrum Access
63Chapter 11 Reinforcement Learning for Network Optimization
19034.3 Aerial Base Station Placement: References
6411.1 Basics of Reinforcement Learning
191Chapter 35 Trusted AI for Networks
6511.1.1 Markov Decision Processes
19235.1 Adversarial Robustness
6611.1.2 Reinforcement Learning Algorithms
19335.2 Explainable Models
6711.1.3 Multi-Armed Bandits
19435.3 Privacy Preservation: References:
6811.2 Network Optimization using RL
195Chapter 36 Network Architecture Innovations with AI
6911.2.1 Routing Optimization
19636.1 Neural Architecture Search
7011.2.2 Congestion Control
19736.2 Meta-learning Based Improvements : References:
7111.2.3 Load Balancing
198Chapter 37 Network Fault Localization and Root Cause Analysis
7211.2.4 Channel Access Optimization
19937.1 Fault Modeling
7311.2.5 Resource Orchestration: References
20037.2 Causality Analysis
74Chapter 12 Case Studies and Practical Examples
20137.3 Automated Remediation Policies: References
7512.1 Real-World Applications of AI and DL in Networking
202Chapter 38 Data Management for AI in Networks
7612.1.1 Network Monitoring and Diagnostics
20338.1 Labeling Challenges
7712.1.2 Intelligent Traffic Engineering
20438.2 Data Pipelines and MLOps
7812.1.3 AI-Defined Networking and Operations
20538.3 Synthetic Data Augmentation: References:
7912.2 Success Stories and Lessons Learned
20639.1 Failure Prediction Models
8012.2.1 Intelligent WAN at Google
20739.2 Automated Fault Diagnosis
8112.2.2 AI-Assisted Network Planning
20839.3 Data-driven Network Redundancy Optimization
8212.2.3 Infrastructure Monitoring at Microsoft: References
20939.4 Intelligent Traffic Engineering
83Chapter 13 Future Outlook and Challenges
21039.5 Self-Healing Systems: References:
8413.1 Research Frontiers with Deep Learning for Networks
211Chapter 40 Privacy and Ethics Considerations in AI for Networks
8513.1.1 Protocol & Architecture Co-Design
21240.1 Privacy Risks of Network Data Mining
8613.1.2 Physics-Informed Learning
21340.2 Federated Learning for Decentralized Data
8713.1.3 Wireless Scene Understanding
21440.3 Differential Privacy Techniques
8813.1.4 Adversarial Robustness & Security
21540.4 Fairness and Bias in Network Analytics
8913.1.5 Federated Learning
21640.5 Ethics of Autonomous Decision Making: References:
9013.1.6 Quantum ML
217Chapter 41 Cloud and Fog Networking
9113.1.7 Neuroevolution & Swarm Learning
21841.1 Resource Optimization in Cloud Data Centers
9213.2 Overcoming Current Limitations
21941.2 Intelligent VM Migration and Consolidation
9313.2.1 Operationalization
22041.3 Traffic Optimization for Geo-distributed Clouds
9413.2.2 Practical Interpretability
22141.4 Latency Improvements with Fog Networking
9513.2.3 Evaluation Rigor : References:
22241.5 Vehicular Networks and Edge Computing: References:
96Chapter 14 Distributed AI for Networking
223Chapter 42 Network Configuration and Management
9714.1 Federated Learning
22442.1 Automated device and policy configuration
9814.1.1 Challenges in Federated Settings
22542.2 Intelligent network provisioning and scaling
9914.1.2 Federated Optimization Algorithms
22642.3 Chatbots and virtual assistants for operators
10014.1.3 Secure Aggregation
22742.4 Natural language interfaces for networks
10114.1.4 Incentive Mechanisms
22842.5 Automated network diagram creation : References
10214.2 Split Learning
229Chapter 43 User and Traffic Forecasting
10314.2.1 Split Neural Network Architectures
23043.1 Time series forecasting techniques
10414.2.2 Secure Channels
23143.2 Origin-destination matrix prediction
10514.3 Distributed Training Techniques
23243.3 Graph-based models for traffic patterns
10614.3.1 Parallelism Granularity
23343.4 Temporal pattern mining in network logs
10714.3.2 Communication Optimizations
23443.5 Client trajectory prediction with RNNs: References
10814.3.3 Consistency & Fault Tolerance : References
235Chapter 44 Network Design and Planning
10915.1 Generative Adversarial Networks
23644.1 Constraint-based optimization
11015.1.1 Adversarial Training
23744.2 Generative network modeling
11115.1.2 Architecture Choices
23844.3 Monte Carlo tree search for design space exploration
11215.1.3 Evaluation Criteria
23944.4 Neural architecture search for protocols
11315.2 Variational Autoencoders
24044.5 Meta-heuristics like genetic algorithms: References
11415.2.1 Latent Variable Modeling
241Chapter 45 Information Centric Networking
11515.2.2 Traffic Generation Process
24245.1 Intelligent in-network caching
11615.3 Invertible Flow Models
24345.2 Content popularity prediction
11715.3.1 Bijective Mappings
24445.3 Adaptive routing based on content
11815.3.2 Network Traffic Flows
24545.4 Natural language queries for content
11915.4 Evaluation Under Real Applications
24645.5 Search on encrypted content: References
12015.5 Challenges and Open Problems: References
247Chapter 46 Intelligent Network Slicing
12116.1 Network Representation Learning
24846.1 Automated slice creation and management
12216.1.1 Encoder Objectives
24946.2 Allocating resources across slices
12316.1.2 Traffic Graph Construction
25046.3 Autoencoder-based anomaly detection
12416.2 Random Walk Graph Embeddings
25146.4 Forecasting slice resource demands
12516.2.1 Random Walk Encoders
25246.5 AI for slice isolation and security
12616.2.2 Improvements
253References: Index
12716.3 Graph Neural Network Encoders
