Intrusion Detection with On-line Clustering Using Reinforcement Learning

Abstract

Today, information technology is growing rapidly, we can obtain all the information much easier. Almost all the important information can be accessed by the users. These conditions raise some new problems, one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification method has high precision, but to get a high precision required a determination of the proper classification model. In this paper, we propose a new approach to detect intrusion with On-line Clustering using Reinforcement Learning. Based on the experimental result, our proposed technique can detect intrusions with high accuracy (99.996% for DoS, 99.939% for Probe, 99.865% for R2L and 99.948% for U2R) and high speed (65 ms)