EMITTER International Journal of Engineering Technology Vol.2, No.1, June 2014 ISSN: 2443-1168 Copyright © 2014 EMITTER International Journal of Engineering Technology - Published by EEPIS 39 Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning Indah Yulia Prafitaning Tiyas, Ali Ridho Barakbah, Tri Harsono, Amang Sudarsono Postgraduate Applied Engineering of Technology Division of Information and Computer Engineering, Department of Information and Computer Engineering, Electronic Engineering Polytechnic Institute of Surabaya EEPIS Campus, Jalan Raya ITS, Sukolilo 60111, Indonesia Email: indahyuliap@yahoo.com, {ridho, amang, trison}@eepis-its.edu Abstract Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises 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 methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms).The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable. Keywords: Intrusion Detection System, On-Line Clustering, Reinforcement Learning, Unsupervised Learning. 1. INTRODUCTION Based on data compiled by CERT [11], the number of intrusions from year to year is increase. From 1995 to 2008, the total attack was summarized by CERT is 46.156, as illustrated in Figure 1.a.