A REVIEW ON ARTIFICIAL INTELLIGENCE BASED INTRUSION DETECTION SYSTEM

A REVIEW ON ARTIFICIAL INTELLIGENCE BASED INTRUSION DETECTION SYSTEM

Mrs.Maria Sowmini¹, Dr.Isaac Sajan R², Dr.M.R.Geetha³, Mrs. Joselin Kavitha⁴

¹ Assistant Professor, CSE, Ponjesly College of Engineering, Nagercoil.

² Professor, ECE, Ponjesly College of Engineering, Email: isaacsajan@gmail.com

³ Professor & Head, ECE, Ponjesly College of Engineering, Nagercoil.

⁴ Assistant Professor ECE, Marthandam College of Engineering & Technology, Kuttakuzhi.

ABSTRACT

Communication technology advances, various and  heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns regarding network security have increased. Accordingly, to deal with potential threats, research on network intrusion detection systems has been actively conducted. Among the various NIDS technologies, recent interest  is focused on artificial intelligence (AI)-based anomaly detection systems, and various models have been proposed to improve the performance of NIDS. However, there still exists the problem of data imbalance, in which AI models cannot sufficiently learn malicious behavior and thus fail to detect network threats accurately. In this study, we propose a novel AI-based NIDS that can efficiently resolve the data imbalance problem and improve the performance of the previous systems. To address the aforementioned problem, we leveraged a state-of-the-art generative model that could generate plausible synthetic data for minor attack traffic.