Advanced EGAN-IDS Framework for Resilience against Adversarial Attacks using Multi-headed Attention Module

Abstract

A network's security depends heavily on its network intrusion detection system (NIDS). Monitoring network traffic continuously and spotting any unusual activity is its main duty. Even though several methods, such as machine learning-based network intrusion detection systems (ML-NIDSs), have been put forth and put into practice to improve the detection of malicious network traffic, many of the studies that have already been done do not adequately replicate real-world situations, where new attack types are continually emerging. Thus, more research is needed to determine how resistant intrusion detection systems are too adversarial and zero-day attacks. We provide and expand the Enhanced Generative Adversarial Network (EGAN-IDS) framework in this study. This framework creates hostile attack routes that are intended to avoid being discovered by five distinct Black Box ML-based intrusion detection systems. Artificial neural networks and self-awareness processes are used by EGAN-IDS to generate artificial adversarial attacks that are able to evade detection. According to our evaluation results, EGAN-IDS has produced adversarial patterns for a variety of attacks, which has reduced detection rates by an average of 15.93% for each of the five IDSs. These conclusions highlight the model's resilience and broad applicability.