Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning

  • Indah Yulia Prafitaning Tiyas Electronic Engineering Polytechnic Institute of Surabaya
  • Ali Ridho Barakbah Electronic Engineering Polytechnic Institute of Surabaya
  • Tri Harsono Electronic Engineering Polytechnic Institute of Surabaya
  • Amang Sudarsono Electronic Engineering Polytechnic Institute of Surabaya


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.


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Kyaw Thet Khaing, Enhanced Features Ranking and Selection using Recursive Feature Elimination(RFE) and k-Nearest Neighbor Algorithms in Support Vector Machine for Intrusion Detection System, International Journal of Network and Mobile Technologies, Vol. 1, Issue 1, pp. 1832-6758, June 2010.

Amir Azimi Alasti, Kaveh Feyzi, Zahra Atashbar Orang, Hadi Bahrbegi, Elnaz Safarzadeh, Using Learning Vector Quantization in Alert Management of Intrusion Detection System, International Journal of Computer Science and Security (IJCSS), Vol. 6, Issue. 2, 2012.

Reyadh Shaker Naoum, Zainab Namh Al-Sultani, Learning Vector Quantization (LVQ) and k-Nearest Neighbor for Intrusion Classification, World of Computer Science and Information Technology Journal (WCSIT), Vol. 2, No. 3, pp. 105-109, 2012.

Manoj Sharma, Keshav Jindal,Ashish Kumar,Intrusion Detection System using Bayesian Approach for Wireless Network, International Journal of Computer Applications, Volume 48– No.5, pp. 0975- 888, June 2012.

A. S. Aneetha and Dr. S. Bose, The Combined Approach for Anomaly Detection using Neural Networks and Clustering Techniques, Computer Science & Engineering An International (CSEIJ), Vol.2, No.4, pp.37-46, August, 2012.

Prof. Dr. Kais Said Al-Sabbagh, Assist. Prof. Hamid M. Ali, Elaf Sabah Abbas, Development an Anomaly Network Intrusion Detection System Using Neural Network, Journal of Engineering, Vol. 18, No. 12, pp. 1325-1334, December, 2012.

A. M. Chandrashekhar, K. Raghuveer, Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector Machines, International Journal of Network Security &Its Applications (IJNSA), Vol.5, No.1, pp. 71-90, January, 2013.

Reyadh Shaker Naoum, Zainab Namh Al-Sultani, Hibrid System of Learning Vector Quantization and Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Classification, International Journal of Research and Reviews in Applied Sciences (IJRRAS), Vol. 14, No. 2, February 2013.

Nitin Mohan Sharma, Tapan P. Gondaliya, Enhance IDS False Alarm Filtering Using KNN Classifier, International Journal of Emerging Research in Management & Technology, Vol. 2, Issue 5, pp. 2278-9359, May 2013.

Ali Ridho Barakbah, Kohei Arai, Pursuit Reinforcement Competitive Learning,Information and Communication Technology Seminar (ICTS), 2006. [accessed on July 28 th , 2013] . [accessed on July28 th , 2013].

How to Cite
Tiyas, I. Y. P., Barakbah, A. R., Harsono, T., & Sudarsono, A. (2014). Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning. EMITTER International Journal of Engineering Technology, 2(1), 39-49.