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