i-manager Publications

Intrusion Detection System in Network Using Particle Swarm Optimization (PSO)

B. Mahalakshmi*, S. Anandamurugan**

*-** PG Scholar, Assistant Professor (SLG), Kongu Engineering College.

Periodicity:January - March'2013
DOI : https://doi.org/10.26634/jse.7.3.2172

Abstract

The network intrusion detection techniques are important to prevent our systems and networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls, user authentication and data encryption have failed to completely protect networks and systems from the increasing the attacks and malwares. Existing system has proposed a new hybrid intrusion detection system by using intelligent dynamic swarm based rough set (IDS-RS) for feature selection and simplified swarm optimization for intrusion data classification. The purpose of this new local search strategy is to get the better solution from the neighborhood of the current solution produced by Simplified Swarm Optimization SSO. Inorder to improve the performance of SSO and Rough Set Theory, Particle Swarm Optimization (PSO) and Enhanced Adaboost is used. It is also used to improve the detection rate and to reduce the false alarm rate.

Keywords

Intrusion Detection, NSLKD Datasets, Particle Swarm Optimization, Adaboost Algorithm.

How to Cite this Article?

Mahalakshmi, B., and Murugan, A. S. (2013). Intrusion Detection System in Network Using Particle Swarm Optimization (PSO). i-manager’s Journal on Software Engineering, 7(3), 24-31. https://doi.org/10.26634/jse.7.3.2172

References

[1]. Kok-Chin Khor, Choo-Yee Ting, Somnuk Phon-Amnuaisuk, (2010). “A cascaded classifier approach forimproving detection rates on rare attack categories in network intrusion detection” Springer Science+Business Media, LLC 2010.

[2]. G.Giacint, and F. Roli, (2002). “Intrusion detection in computer networks by multiple classifier systems” 390 –393 Vol.2, 2002.

[3]. Weiming Hu (2008). “AdaBoost based Intrusion Detection System” Vol. 38, No. 2, 2008.

[4]. S. Chebrolu, A. Abraham, and J. P. Thomas, (2005). “Feature deduction and ensemble design of intrusion detection systems,” Comput. Secur., Vol. 24, No. 4, pp.295–307, Jun. 2005.

[5]. W. Lee, S. J. Stolfo, and K. Mok, (1999). “A data mining framework for building intrusion detection models,” in Proc. IEEE Symp. Secur. Priv., May 1999, pp. 120–132.

[6]. S. Mukkamala, A. H. Sung, and A. Abraham, (2005). “Intrusion detection using an ensemble of intelligent paradigms,”J. Netw. Comput. Appl., Vol. 28, No. 2, pp. 167–182,Apr. 2005.

[7]. Pfahringer, (2000). “Winning the kdd99 classification cup: Bagged boosting,” SIGKDD Explor., Vol. 1, No. 2, pp.65–66, 2000.

[8]. P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez, and E.Vazquez, (2009). “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Computers & Security, Vol. 28, pp. 18-28,2009.

[9]. Y. Guan, A. A. Ghorbani, and N. Belacel, (2003). “Y -means: A clustering method for intrusion detection,” in Proc. IEEE Can. Conf. Electr. Comput. Eng.,2003, Vol. 2, pp.1083–1086.

[10]. Yongiin Liu, Na Li, Leina Shi and Fangping Li, (2010). “An Intrusion Detection Method Based on Decision Tree,”International Conference on E-Health Networking, Digital Ecosystems and Technologies”, pp. 232-235,2010.

[11]. Hongyu Yang, Feng Xie , Yi Lu, (2006). Tianjin Key Lab for Adv. Signal Process., Civil Aviation Univ. of China, Tianjin “Research on Network anomaly Detection Based on Clustering and Classifier ” IEEE Transaction on Computational Intelligence and Security, 2006.

[12]. N. Friedman, D. Geiger and M. Goldsmidt, (1997). “Bayesian Network Classifiers,” Machine Learning, Vol.29, pp131-163, Nov 1997.

[13]. J.W. Han and M.Kamber, (2006). Data Mining: Concepts and Techniques, 2nd edition. Morgan Kaufmann. 2006.

[14]. A.Zainal, M.A. Maarof, S.M. Shamsuddin, (2007). Feature selection using rough-DPSO in anomaly intrusion detection, in: LNCS, vol. 4705, Springer-Verlag, Berlin-Heidelberg, 2007, pp. 512–524.

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Intrusion Detection System in Network Using Particle Swarm Optimization (PSO)

Abstract

Keywords

How to Cite this Article?

References

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