Fortifying Machine Learning-Powered Intrusion Detection: A Defense Strategy Against Adversarial Black-Box Attacks

Intrusion detection systems (IDSs) powered by machine learning (ML) techniques have greatly advanced in detecting security threats. However, with advancements come new challenges and vulnerabilities. As most of the ML-based IDSs prioritize performance, their robustness and security are in question, which becomes an advantage for bad actors to exploit the vulnerabilities and target IDS models. It is a serious concern in the cybersecurity domain. This paper emphasizes the impact of adversarial machine learning (AML)—a well-known vulnerability of ML techniques—and proposes a defense mechanism to improve the robustness of ML-powered IDSs to adversarial black-box attacks. The primary motive of this research is to defend the defenders of cybersecurity, in particular, the IDSs. The paper provides a background of the elements involved in the experimentation, presents the methodology followed for the experiments, and analyzes the evaluation results obtained. Further, it discusses the directions and possibilities for future endeavors.