Network Intrusion Detection Using Hybrid Simplified Swarm Optimization Technique

International Journal of P2P Network Trends and Technology (IJPTT)          
© 2013 by IJPTT Journal
Volume-3 Issue-5                           
Year of Publication : 2013
Authors : S. Revathi, A. Malathi


S. Revathi, A. Malathi."Network Intrusion Detection Using Hybrid Simplified Swarm Optimization Technique". International Journal of P2P Network Trends and Technology (IJPTT). V3(5):6 - 10  Sep - Oct 2013, ISSN:2249-2615, Published by Seventh Sense Research Group.


— Network security risks grow tremendously in recent past, the attacks on computer networks have enhanced hugely and need economical network intrusion detection mechanisms. Data processing and machine-learning techniques are used for network intrusion detection throughout the past few years and have gained abundant quality. In this paper, we propose an intrusion detection mechanism based on simplified particle swarm optimization (SSO) is used to investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. SSO is used to find more appropriate set of attributes for classifying network intrusions, and also used as a classifier. In preprocessing step, we reduce the dimensions of the dataset by using various dimension reduction techniques, and then this reduced dataset is offered to the proposed hybrid SSO approach that further optimizes the dimensions of the data and finds an optimal set of features. SSO is an optimization method that has a strong global search capability and is used for dimension optimization. The analysis performed on standard KDD cup99 dataset which contain various kind of intrusion. The experimental results shows the worth of the proposed approach by using different performance metrics.


[1]. Deris tiawan, Abdul Hanan Abdullah, Mohd. Yazid dris, “Characterizing Network Intrusion Prevention System”, International Journal of Computer Applications (0975 – 8887), Volume 14– No.1, (January 2011).
[2]. J. Han and M. Kamber, Data Mining: Concepts and Techniques. San Fransisco: Morgan Kaufmann, 2001.
[3]. C. Grosan, A. Abraham, and M. Chris, "Swarm Intelligence in Data Mining," Studies in Computational Intelligence, vol. 34, pp. 1-20, Springer-Verlag: Berlin Heidelberg, 2006.
[4]. R. C. Eberhart and Y. Shi, "Particle swarm optimization: developments, applications and resources," in Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, May 27-30, vol. 1, pp. 81-86, 2001.
[5]. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, vol. 3, pp. 1157 - 1182, Mar.2003.
[6]. R. Bello, Y. Gomez, A. Nowe, and M. M. García, "Two step particle swarm optimization to solve the feature selection problem," in Proceedings of The 7th International Conference on Intelligent Systems Design and Applications, Rio de Janeiro, Brazil, Oct. 22-24, pp. 691-696, 2007.
[7]. C. S. Yang, L. Y. Chuang, J. C. Li, and C. H. Yang, "Chaotic maps in binary particle swarm optimization for feature selection," in Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications, Muroran, Japan, June 25-27, pp. 107-112, 2008.
[8]. KDDCUP 99 dataset, available at:
[9]. MIT Lincoln Labs, 1998 DARPA Intrusion Detection Evaluation. Available on: val/ index.html, February 2008.
[10]. G. Sunil Kumar, C.V.K Sirisha, Kanaka Durga.R, A.Devi, “Robust Pre-processing and Random Forests Technique for Network Probe Anomaly Detection”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-1, Issue-6, (January 2012).


- Swarm intelligence, Simplified Swarm Optimization, optimization, Data mining, Intrusion Detection.