SGAN-IDS: Self-Attention-Based Generative Adversarial Network against Intrusion Detection Systems

SGAN-IDS: Self-Attention-Based Generative Adversarial Network against Intrusion Detection Systems

Blog Article

In cybersecurity, a network intrusion detection system (NIDS) is a critical component in networks.It monitors Knitting Notions network traffic and flags suspicious activities.To effectively detect malicious traffic, several detection techniques, including machine learning-based NIDSs (ML-NIDSs), have been proposed and implemented.However, in much of the existing ML-NIDS research, the experimental settings do not accurately reflect real-world scenarios where new attacks are constantly emerging.

Thus, the robustness of intrusion detection systems against zero-day and adversarial attacks is a crucial area that requires further investigation.In this paper, we introduce and develop a framework named SGAN-IDS.This framework constructs adversarial attack flows designed to evade detection by five BlackBox ML-based IDSs.SGAN-IDS employs generative adversarial networks and self-attention mechanisms to generate synthetic adversarial attack flows that are resilient to detection.

Our evaluation results demonstrate that LIPOSOMAL VITAMIN C 1000 SGAN-IDS has successfully constructed adversarial flows for various attack types, reducing the detection rate of all five IDSs by an average of 15.93%.These findings underscore the robustness and broad applicability of the proposed model.