Robust Deep Learning for Network Intrusion Detection Under Adversarial Attacks

As modern computer networks become increasingly complex, they face growing exposure to sophisticated cyberattacks. Network Intrusion Detection Systems (NIDS) play a vital role in identifying malicious activities and protecting digital infrastructure. Deep learning has improved intrusion detection by uncovering complex traffic patterns and achieving high accuracy; however, these models remain vulnerable to adversarial attacks that can compromise their reliability and effectiveness. This challenge is particularly significant in environments where accurate and timely threat detection is critical. As a result, developing robust deep learning frameworks capable of resisting adversarial manipulations while maintaining strong detection performance has become an important research priority. Advancing resilience in AI-driven intrusion detection systems is essential for strengthening cybersecurity defenses in adversarial environments. Robust Deep Learning for Network Intrusion Detection Under Adversarial Attacks provides a comprehensive examination of adversarial examples and machine learning threats targeting deep learning-based NIDS. The book explores how attacks are generated against common architectures, how they transfer across models, how they persist despite preprocessing techniques, and how defensive mechanisms perform against adaptive adversaries. Covering topics such as artificial intelligence, encryption, and threat detection, this book serves as a valuable resource for engineers, academics, researchers, practitioners, and scientists seeking to enhance the security and resilience of intelligent intrusion detection systems.