A Review of Network Intrusion Detection System using machine learning algorithms - Kumar, Ravinder (International Journal of Computer Sciences and Engineering, 2025)
With the advancement in the communication technology, the probability of external attacks through networks is increasing day by day. Therefore, Intrusion Detection System (IDS) had became very important and an emerging area of research which, attempts to identify and notify the activities of users as normal (or) anomaly. IDS are a nonlinear and complicated problem and deals with network traffic data. Many IDS methods have been proposed and produce different levels of accuracy. That is why the development of effective and robust Intrusion detection system is necessary. This paper presents a state of the art of intrusion detection system (IDS) classification techniques using various machine learning algorithms. Experiments have been conducted to evaluate the performance of various well known machine learning algorithms on NSL-KDD data set.