11231nam 22005413 450 991104241180332120251208110123.01-394-28517-51-394-28519-11-394-28518-3(CKB)42587030300041(MiAaPQ)EBC32409831(Au-PeEL)EBL32409831(OCoLC)1551396490(CaSebORM)9781394285167(OCoLC)1551293170(OCoLC-P)1551293170(EXLCZ)994258703030004120251115d2026 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning for Intrusion Detection Techniques and Applications1st ed.Newark :John Wiley & Sons, Incorporated,2026.©2026.1 online resource (333 pages)1-394-28516-7 Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Foreword -- Preface -- Acknowledgments -- Chapter 1 Intrusion Detection in the Age of Deep Learning: An Introduction -- 1.1 Introduction -- 1.1.1 The Pioneers of Network Security -- 1.1.1.1 Limitations of the Existing System -- 1.1.2 How Firewalls Are Different from IDS -- 1.1.3 Need for Intrusion Detection Systems -- 1.1.4 Intrusion Detection System -- 1.1.4.1 Intrusion Detection Technologies -- 1.1.4.2 Intrusion Detection Methodologies -- 1.1.4.3 Intrusion Detection Approaches -- 1.1.5 Need for Deep Learning Based IDS -- References -- Chapter 2 Machine Learning for Intrusion Detection -- 2.1 Introduction -- 2.1.1 Overview of Intrusion Detection Systems (IDSs) -- 2.1.1.1 Types of IDSs: Host‐Based, Network‐Based, Hybrid -- 2.2 Role of Machine Learning in IDSs -- 2.2.1 Benefits and Challenges of Using Machine Learning in IDSs -- 2.2.1.1 Benefits of ML in IDSs -- 2.2.1.2 Challenges of ML in IDS -- 2.2.2 Evolution from Traditional Methods to ML‐Based Approaches in IDSs -- 2.2.2.1 Traditional Methods in IDSs -- 2.2.2.2 Transition to ML‐Based Approaches -- 2.2.2.3 Current ML‐Based IDS Landscape -- 2.3 Fundamentals of Machine Learning -- 2.3.1 Key ML Techniques -- 2.3.1.1 How These Concepts Enable Pattern and Anomaly Detection -- 2.3.2 Key Algorithms Used in Intrusion Detection -- 2.3.3 Classification Algorithms -- 2.3.3.1 Clustering Algorithms -- 2.3.3.2 Anomaly Detection Algorithms -- 2.4 Data Preparation for IDSs -- 2.4.1 Types of Data Used in IDSs -- 2.4.2 Data Preprocessing Techniques -- 2.5 Supervised Learning for Intrusion Detection -- 2.5.1 Key Components of Supervised Learning -- 2.5.2 Benefits of Supervised Learning in IDSs -- 2.5.3 Challenges of Supervised Learning in IDSs -- 2.5.4 Common Supervised Learning Techniques in IDSs.2.5.5 Supervised Learning Algorithms -- 2.5.6 Practical Example: Using Supervised Learning in IDSs -- 2.6 Unsupervised Learning for Intrusion Detection Systems (IDSs) -- 2.6.1 Techniques and Algorithms -- 2.6.2 Example Use Case: Anomaly‐Based Network Intrusion Detection -- 2.7 Semi‐Supervised Learning in Intrusion Detection Systems (IDSs) -- 2.7.1 Semi‐Supervised Algorithms and Applications -- 2.7.2 Applications in IDSs -- 2.7.3 Example Use Case: Semi‐Supervised Network Intrusion Detection -- 2.8 Reinforcement Learning for Intrusion Detection System -- 2.8.1 Example Scenario -- 2.9 Feature Engineering, Model Training, and Hyperparameter Tuning in IDS -- 2.9.1 Feature Engineering in IDS -- 2.9.2 Model Training in IDS -- 2.9.3 Hyperparameter Tuning in IDSs -- 2.9.4 Practical Implementation Challenges in IDSs -- References -- Chapter 3 Deep Learning Fundamentals‐I -- 3.1 Introduction to Deep Learning -- 3.1.1 Definition and Importance -- 3.1.2 Deep Learning in Cybersecurity: Enhancing Threat Detection and Prevention -- 3.1.3 Key Areas Where Deep Learning Enhances Cybersecurity -- 3.1.3.1 Proactive Threat Detection with Deep Learning -- 3.2 Conceptual Foundations of Deep Learning -- 3.2.1 Historical Evolution of Deep Learning -- 3.2.2 Key Differences Between Deep Learning and Traditional Machine Learning -- 3.2.3 Why Deep Learning Is Suited for Intrusion Detection -- 3.2.4 Artificial Neural Networks (ANNs) as the Core of Deep Learning -- 3.2.4.1 Structure of ANNs -- 3.2.4.2 Working Mechanism of ANNs -- 3.2.4.3 The Role of Deep Learning in Pattern Recognition and Anomaly Detection -- 3.3 Neural Networks: The Building Blocks of Deep Learning -- 3.3.1 Biological Inspiration and Mathematical Representation -- 3.3.2 Architecture of Neural Networks (Layers, Activation Functions, and Weights) -- 3.3.2.1 Layers in Neural Networks.3.3.2.2 Neuron Activation Function -- 3.3.2.3 Types of Activation Functions -- 3.3.3 Training Deep Learning Models Using Backpropagation and Weight Optimization -- 3.3.3.1 Error Functions in Neural Networks -- 3.3.3.2 Steps in Backpropagation -- 3.3.4 Gradient Descent: The Backbone of Learning in Neural Networks -- 3.3.4.1 Advanced Optimization Techniques -- 3.3.5 Regularization Techniques in Neural Networks -- 3.3.5.1 L1 and L2 Regularization -- 3.3.6 Dropout: Reducing Overfitting -- 3.3.6.1 Impact of Activation Functions and Optimization on Deep Learning -- 3.4 Applications of Deep Learning in Intrusion Detection -- 3.4.1 Types of Cyber Threats and Attacks -- 3.4.1.1 DDoS Attacks -- 3.4.1.2 Malware and Ransomware -- 3.4.1.3 Brute Force Attacks -- 3.4.1.4 Insider Threats -- 3.4.2 Deep Learning‐Based Intrusion Detection Systems (IDSs) -- 3.4.2.1 Signature‐Based IDS -- 3.4.2.2 Anomaly‐Based IDS -- 3.4.2.3 Deep Learning Models Commonly Used for IDSs -- 3.4.3 Case Studies and Real‐World Implementations -- 3.4.3.1 Financial Institutions -- 3.4.3.2 Technology Companies -- 3.4.3.3 Healthcare Organizations -- 3.4.3.4 Government Agencies -- 3.4.3.5 Retail and E‐Commerce -- 3.5 Security‐Enhancing Potential of Deep Learning -- 3.5.1 Advantages of Deep Learning in Cybersecurity -- 3.5.1.1 Automated Threat Detection -- 3.5.1.2 High Accuracy -- 3.5.1.3 Scalability -- 3.5.1.4 Adaptability to Evolving Threats -- 3.5.1.5 Reduced False Positives -- 3.5.2 Challenges and Limitations of Deep Learning‐Based IDS -- 3.5.2.1 Computational Costs -- 3.5.2.2 Adversarial Attacks -- 3.5.2.3 Data Availability and Quality -- 3.5.3 Future Directions in AI‐Driven Intrusion Detection -- 3.5.3.1 Federated Learning -- 3.5.3.2 Explainable AI (XAI) -- 3.5.3.3 Integration with Blockchain -- 3.5.3.4 Continuous Learning and Adaptation -- 3.6 Conclusion -- 3.6.1 Summary of Key Insights.3.6.2 Future Directions in Deep Learning for Cybersecurity -- References -- Chapter 4 Deep Learning Fundamentals‐II -- 4.1 Introduction -- 4.2 Artificial Neural Networks -- 4.3 Overview of Deep Learning -- 4.4 Deep Learning Algorithms -- 4.4.1 Deep Neural Networks (DNNs) -- 4.4.2 Deep Belief Networks -- 4.4.3 Autoencoders -- 4.4.4 Convolutional Neural Network -- 4.4.5 Recurrent Neural Networks -- 4.5 Conclusion -- References -- Chapter 5 Intrusion Detection Through Deep Learning: Emerging Trends and Challenges -- 5.1 Introduction -- 5.2 Deep Learning -- 5.2.1 Neural Network Architectures -- 5.2.2 Types of Neural Networks -- 5.2.2.1 Feed‐forward Neural Networks (FNNs) -- 5.2.2.2 Convolutional Neural Networks (CNNs) -- 5.2.2.3 Recurrent Neural Networks (RNNs) -- 5.2.2.4 Recursive Neural Networks (RvNNs) -- 5.3 Applications of Deep Learning -- 5.4 Intrusion Detection -- 5.4.1 Classification -- 5.5 Methodologies of Detection -- 5.6 Deep Learning for Intrusion Detection -- 5.7 Limitations -- 5.7.1 Mr. William's Case -- 5.7.2 Challenges -- 5.8 Conclusion -- References -- Chapter 6 Dataset for Evaluating Deep Learning‐Based Intrusion Detection -- 6.1 Introduction -- 6.2 Data -- 6.2.1 Packet‐Based Data -- 6.2.2 Flow‐Based Data -- 6.2.3 Other Data -- 6.3 Dataset Properties -- 6.3.1 Basic Information -- 6.3.2 Nature of Data -- 6.3.3 Data Volume -- 6.3.4 Recording Environment -- 6.3.5 Evaluation -- 6.4 Datasets -- 6.4.1 DARPA -- 6.4.2 KDD 1999 -- 6.4.3 NSL‐KDD -- 6.4.4 ISCX‐2012 -- 6.4.5 UNSW‐NB15 -- 6.4.6 CIC‐IDS‐2017 -- 6.5 Conclusion -- References -- Chapter 7 Deep Learning Features: Techniques for Extraction and Selection -- 7.1 Introduction -- 7.1.1 Overview of Intrusion Detection Systems (IDSs) -- 7.1.2 Role of Deep Learning in IDSs -- 7.1.3 Importance of Feature Extraction and Selection -- 7.1.3.1 Feature Extraction -- 7.1.3.2 Feature Selection.7.1.3.3 Critical Role in IDSs -- 7.1.4 Improvement in Accuracy, Complexity Reduction, and Efficiency Enhancement -- 7.1.5 Challenges in Managing High‐Dimensional Data in IDSs -- 7.2 Techniques for Feature Extraction and Selection -- 7.2.1 Principal Component Analysis -- 7.2.2 Linear Discriminant Analysis -- 7.2.3 Mutual Information -- 7.2.3.1 How Mutual Information Works? -- 7.2.4 Chi‐Squared Feature Selection -- 7.2.4.1 How Chi‐Squared Feature Selection Works? -- 7.2.5 Comparative Analysis of Techniques -- 7.3 Applications in Intrusion Detection Systems -- 7.3.1 Integrating Feature Extraction and Selection in IDS Workflows -- 7.3.1.1 Impact on Performance -- 7.3.1.2 Challenges in Real‐World Applications -- 7.3.2 Performance Improvements -- 7.3.2.1 Efficiency Gains Through MI and Chi‐Squared Methods -- 7.3.2.2 Enhancing Scalability for Growing Network Demands -- 7.3.3 Practical Deployment -- 7.3.3.1 Preprocessing with PCA and LDA -- 7.3.3.2 Training with MI and Chi‐Squared Methods -- 7.3.3.3 Hybrid Approaches for Enhanced Results -- 7.3.3.4 Real‐World Applications -- 7.4 Conclusion and Future Trends -- 7.4.1 Key Insights -- 7.4.2 Future Directions -- References -- Chapter 8 Exploring Advanced Artificial Intelligence for Anomaly Detection -- 8.1 Introduction -- 8.1.1 Types of Anomalous Detection -- 8.1.2 Artificial Intelligence‐Based Anomaly Detection -- 8.1.2.1 AI‐Based AD Process -- 8.1.2.2 Machine Learning Algorithms for AD -- 8.1.2.3 Application Domains -- 8.1.2.4 Advantages of AI‐Based AD Methods -- 8.1.2.5 Challenges in AI‐Based AD -- 8.1.2.6 AI‐Based AD Methods -- 8.2 Autoencoder‐Based Anomaly Detection -- 8.2.1 Types of Autoencoders -- 8.3 Generative Adversarial Networks Anomaly Detection -- 8.3.1 Features of GANs -- 8.3.2 Working Principle of GANs -- 8.4 One‐Class Classification Anomaly Detection.8.5 Deep Reinforcement Learning Anomaly Detection.Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks Deep Learning for Intrusion Detection provides a practical guide to understand the challenges of intrusion detection in various application areas and how deep learning can be applied to address.Intrusion detection systems (Computer security)Intrusion detection systems (Computer security)005.8/4Masoodi Faheem Syeed1857777MiAaPQMiAaPQMiAaPQBOOK9911042411803321Deep Learning for Intrusion Detection4458794UNINA