A Comprehensive Review: The Evolving Cat-and-Mouse Game in Network Intrusion Detection Systems Leveraging Machine Learning

Machine learning (ML) techniques have significantly enhanced decision support systems to render them more accurate, efficient, and faster. ML classifiers in securing networks, on the other hand, face a disproportionate risk from the sophisticated adversarial attacks compared to other areas, such as spam filtering, intrusion, and virus detection, and this introduces a continuous competition between malicious users and preventers. Attackers test ML models with inputs that have been specifically crafted to evade these models and obtain inaccurate forecasts. This paper presents a comprehensive review of attack and defensive techniques in ML-based NIDSs. It highlights the current serious challenges that the systems face in preserving robustness against adversarial attacks. Based on our analysis, with respect to their current superior performance and robustness, ML-based NIDS require urgent attention to develop more robust techniques to withstand such attacks. Finally, we discuss the current existing approaches in generating adversarial attacks and reveal the limitations of current defensive approaches. In this paper, the most recent advancements, such as hybrid defensive techniques that integrate multiple strategies to prevent adversarial attacks in NIDS, have highlighted the ongoing challenges.