A Review on Machine Learning Based Approaches of Network Intrusion Detection Systems

Abstract

The rapid growth of using the Internet raises the possibility of network attacks. In order to secure internal networks, intrusion detection systems are widely employed to address a major research challenge in network security, which aims to efficiently detect unusual access or attacks. To do so, various intrusion detection systems approaches based on the concepts of machine learning algorithms have been developed in the literature to tackle computer security threats. These IDs approaches can be broadly classified into Signature-based Intrusion Detection Systems and Anomaly-based Intrusion Detection Systems. This review paper presents a taxonomy of current intrusion detection systems (IDs), a comprehensive review of significant recent works, and a variety of recent attacks that can be detected in the network environment.