Defenses for Adversarial attacks in Network Intrusion Detection System – A Survey

Abstract

In computer security, machine learning has a greater impact in recent years. Ranging from spam filtering, malware analysis, and traffic analysis to network security the usage of machine learning algorithms are manifold. In the area of network security, machine learning techniques are used especially in developing intrusion detection systems. There are basically two kinds of intrusion detection systems - host intrusion detection systems and network intrusion detection systems. Even though machine learning techniques have greatly improved the efficiency of the intrusion detection systems, they are vulnerable to adversarial attacks which are designed and launched by adaptive adversaries who know the working principles of machine learning models. In recent years adversarial machine learning has gained attention in the domain of machine learning in which attackers exploit the inherent fallacies in the assumptions made in the machine learning models. In the domain of network security especially in intrusion detection systems, the significant role of adversarial machine learning has not been addressed in detail. This survey examines different types of defenses deployed to mitigate the impact of adversarial attacks. Their effectiveness in dealing with attacks is analysed and their limitations are discussed.