Generative AI in Network Security and Intrusion Detection

Abstract

Protecting virtual assets from cyber threats is essential as we live in a digitally advanced world. Providing a responsible emphasis on proper network security and intrusion detection is imperative. On the other hand, traditional strategies need a supportive tool to adapt to the transforming threat space. New generative AI techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) are the mainstream technologies required to meet the gap. This chapter deals with how these models can enhance network security by inspecting the network traffic for anomalies and malicious behaviors detected through unsupervised learning, which considers strange or emerging phenomena. This survey features innovations in fault detection, behavior control, deep packet inspection, traffic classification, and examples of real-world intrusions detected by GAN-based systems. Furthermore, the chapter focuses on the challenges of adversarial attacks on models that require the development of solid defense mechanisms, such as generative adversarial networks. Ethics becomes the following matter on our list of discussions, given that privacy transparency and accountability are to be observed when working with generative AI technologies in network security. Finally, the authors examine trends that determine how cyber-attacks are dealt with comprehensively.