book

Deep Learning for Intrusion Detection

Name: Deep Learning for Intrusion Detection
ISBN: 9781394285167

by Faheem Syeed Masoodi, Alwi Bamhdi

December 2025

Intermediate to advanced

336 pages

9h 21m

English

Wiley

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Cover
Table of Contents
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Foreword
Preface
Acknowledgments

1 Intrusion Detection in the Age of Deep Learning: An Introduction
1.1 IntroductionReferences
2 Machine Learning for Intrusion Detection
2.1 Introduction2.2 Role of Machine Learning in IDSs2.3 Fundamentals of Machine Learning2.4 Data Preparation for IDSs2.5 Supervised Learning for Intrusion Detection2.6 Unsupervised Learning for Intrusion Detection Systems (IDSs)2.7 Semi-Supervised Learning in Intrusion Detection Systems (IDSs)2.8 Reinforcement Learning for Intrusion Detection System2.9 Feature Engineering, Model Training, and Hyperparameter Tuning in IDSReferences
3 Deep Learning Fundamentals-I
3.1 Introduction to Deep Learning3.2 Conceptual Foundations of Deep Learning3.3 Neural Networks: The Building Blocks of Deep Learning3.4 Applications of Deep Learning in Intrusion Detection3.5 Security-Enhancing Potential of Deep Learning3.6 ConclusionReferences
4 Deep Learning Fundamentals-II
4.1 Introduction4.2 Artificial Neural Networks4.3 Overview of Deep Learning4.4 Deep Learning Algorithms4.5 ConclusionReferences
5 Intrusion Detection Through Deep Learning: Emerging Trends and Challenges
5.1 Introduction5.2 Deep Learning5.3 Applications of Deep Learning5.4 Intrusion Detection5.5 Methodologies of Detection5.6 Deep Learning for Intrusion Detection5.7 Limitations5.8 ConclusionReferences
6 Dataset for Evaluating Deep Learning-Based Intrusion Detection
6.1 Introduction6.2 Data6.3 Dataset Properties6.4 Datasets6.5 ConclusionReferences
7 Deep Learning Features: Techniques for Extraction and Selection
7.1 Introduction7.2 Techniques for Feature Extraction and Selection7.3 Applications in Intrusion Detection Systems7.4 Conclusion and Future TrendsReferences
8 Exploring Advanced Artificial Intelligence for Anomaly Detection
8.1 Introduction8.2 Autoencoder-Based Anomaly Detection8.3 Generative Adversarial Networks Anomaly Detection8.4 One-Class Classification Anomaly Detection8.5 Deep Reinforcement Learning Anomaly Detection8.6 Recurrent Neural Networks-Based Anomaly Detection8.7 Transfer Learning Anomaly Detection8.8 ConclusionReferences
9 Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach
9.1 Introduction9.2 Automation: Autonomous Decision-Making for Efficiency9.3 Data Collection and Analytics: Leveraging Big Data for Optimization9.4 Data Privacy and Integrity9.5 Authentication and Access Control9.6 Intrusion Detection9.7 Adaptability to Evolving Threats9.8 The Role of Deep Learning in Security Enhancement9.9 Challenges and Future Directions9.10 ConclusionReferences
10 Deep Learning-Based Intrusion Detection in Wireless Networks
10.1 Introduction10.2 The Importance of Security in Wireless Networks10.3 Challenges of Intrusion Detection Systems (IDS) in MANETs and WSNs10.4 Intrusion Detection Systems10.5 Applications of Deep Learning in Bolstering Security Across Wireless Networks10.6 Deep Learning-Based Solutions for the Challenges of Intrusion Detection in MANETs and WSNsReferences
11 Deep Learning-Based Intrusion Detection in Wireless Networks
11.1 Introduction11.2 Wireless Network Security and IDS Challenges11.3 Deep Learning for Intrusion Detection11.4 Common Deep Learning Architectures for IDS11.5 Applications of Deep Learning-Based IDS11.6 Challenges and Future Research Directions11.7 ConclusionReferences
12 Securing IoT Environments: Deep Learning-Based Intrusion Detection
12.1 Introduction12.2 Overview of IoT Security Challenges12.3 Deep Learning for Intrusion Detection in IoT12.4 Ensuring Data Safety and Privacy in Deep Learning-Based Intrusion Detection Systems (IDSs)12.5 IoT Operations with Security Optimization12.6 Challenges and Future Directions12.7 ConclusionReferences
13 A Deep Learning Approach for the Detection of Zero-day Attacks
13.1 Introduction13.2 Network Vulnerabilities13.3 Anomalies and Anomalies in Networks13.4 Deep Learning13.5 Vulnerabilities in Hardware and Software13.6 Network Configuration Vulnerabilities13.7 Network Hardware Vulnerabilities13.8 Network Perimeter Vulnerabilities13.9 Network Monitoring and Logging Vulnerabilities13.10 Communication Vulnerabilities13.11 Wireless Connection Vulnerabilities13.12 Cyberattacks That Exploit Vulnerabilities13.13 Denial of Service (DoS)13.14 User to Root (U2R) Attacks13.15 Remote to Local (R2L) Attacks13.16 Probe Attacks13.17 Deep Learning to Detect and Mitigate Zero-day AttacksReferences
Index
End User License Agreement

Content preview from Deep Learning for Intrusion Detection

9Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach

Syed Irfan Yaqoob¹, Preet Kamal¹, Shivani Aggarwal², Anuradha Kanade³, and Shantanu Kanade⁴

¹CSE, AIT Chandigarh University, Mohali, Punjab, India²Department of Computer Science Engineering IIT Ropar Campus, Rupnagar, Bara Phool, Punjab, India³Computer Science and Applications, School of Computer Science and Engineering MIT WPU, Pune, Maharashtra, India⁴Symbiosis School for Online and Digital Learning, Symbiosis International University Lavale, Pune, Maharashtra, India

9.1 Introduction

Smart environments—which include smart homes, cities, and industrial Internet of Things (IoT) systems—create a new way of life by integrating a comprehensive network of interconnected and intercommunicating devices and sensors that exchange data in real time. These systems are specially designed to improve efficiency, convenience, and sustainability. In smart homes, the heating, lighting, and security systems can be automated, while in smart cities, sensors monitor traffic, air quality, and public services (Figure 9.1). On an industrial level, industrial IoT optimizes manufacturing processes by collecting data about machines and production lines to increase productivity and safety [1]. These smart environments rely on a continuous data exchange system whereby devices interact with each other and centralized cloud platforms. Interconnectivity is a primary enabler of performance optimization, energy efficiency, ...

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Deep Learning for Intrusion Detection

by Faheem Syeed Masoodi, Alwi Bamhdi

9Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach

9.1 Introduction

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