Deep Learning for Intrusion Detection
John Wiley & Sons Inc (Verlag)
978-1-394-28516-7 (ISBN)
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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 those challenges. It begins by discussing the basic concepts of intrusion detection systems (IDS) and various deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Later chapters cover timely topics including network communication between vehicles and unmanned aerial vehicles. The book closes by discussing security and intrusion issues associated with lightweight IoTs, MQTT networks, and Zero-Day attacks.
The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice.
Deep Learning for Intrusion Detection includes information on:
Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets
The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems
Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities
Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority
Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity.
FAHEEM SYEED MASOODI, PHD, is an Associate Professor of Cybersecurity at Bahrain Polytechnic University. He previously served at the University of Kashmir and the Jazan University in Saudi Arabia. He holds a PhD in Network Security and Cryptography and has published extensively in cryptography, intrusion detection, post-quantum cryptography, financial security, and IoT. His contributions include several books, high-impact papers, and fellowships from France, Brazil, India, and Malaysia. ALWI BAMHDI, PHD, is an Associate Professor in the Computer Sciences Department at Umm ul Qura University, Saudi Arabia. His research interests include mobile ad hoc networks, wireless sensor networks, and information security.
List of Contributors xvii
1 Intrusion Detection in the Age of Deep Learning: An Introduction 1
2 Machine Learning for Intrusion Detection 25
3 Deep Learning Fundamentals-I 59
4 Deep Learning Fundamentals-II 91
5 Intrusion Detection Through Deep Learning: Emerging Trends and Challenges 107
6 Dataset for Evaluating Deep Learning-Based Intrusion Detection 125
7 Deep Learning Features: Techniques for Extraction and Selection 147
8 Exploring Advanced Artificial Intelligence for Anomaly Detection 167
9 Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach 185
10 Deep Learning-Based Intrusion Detection in Wireless Networks 209
11 Deep Learning-Based Intrusion Detection in Wireless Networks 233
12 Securing IoT Environments: Deep Learning-Based Intrusion Detection 251
13 A Deep Learning Approach for the Detection of Zero-day Attacks 267
Index 285
| Erscheint lt. Verlag | 23.3.2026 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 160 x 231 mm |
| Gewicht | 567 g |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-394-28516-7 / 1394285167 |
| ISBN-13 | 978-1-394-28516-7 / 9781394285167 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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