Intrusion Detection System Based on Hybrid Feature Selection and Support Vector Machine (HFS-SVM)

Abstract:

In recent years, anomaly based intrusion detection techniques are continuously developed and a support vector machine (SVM) is one of the technique. However, it requires training time and storage if there are lots of numbers of features. In this paper, a hybrid feature selection, using Correlation based on Feature Selection and Motif Discovery using Random Projection techniques, is proposed to reduce the number of features from 41 to 3 features with KDD'99 dataset. It is compared with a regular SVM technique with 41 features. The results show that the accuracy rate is also high at 98% and the training time is less than the regular SVM almost by half.

• Periodical: Applied Mechanics and Materials (Volume 781)
• Pages: 125-128
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.781.125
• Online since: August 2015
• Authors: Yonchanok Khaokaew*, Tanapat Anusas-Amornkul, Koonlachat Meesublak
• Keywords: Anomaly Detection, Correlation-Based Feature Selection, Motif Discovery, Support Vector Machine

References

[1] A. Bivens, C. Palagiri, R. Smith, B. Szymanski and M. Embrechts, Network-based Intrusion Detection Using Neural Networks, Proc. Intelligent Engineering system through Artificial Neural Networks ANNIE. (2002).

[2] C. Cortes and V. Vapnik., Support Vector Networks, in: Machine learning, Kluwer Academic Publishers, Boston, 1995, pp.273-297.

[3] M. A. Hall, Correlation-based Feature Selection for Machine Learning, Ph.D. dissertation, Dept. Com. Sci., Waikato Univ., Hamilton, New Zealand, (1999).

[4] J. Buhler and M. Tompa: submitted to Journal of Computational Biology (2002).

[5] T. Shon, Y. Kim, C. Lee, and J. Moon, A Machine Learning Framework for Network Anomaly Detection using SVM and GA, Proceedings of the 2005 IEEE Workshop on Information Assurance and Security United States Military Academy. (2005).

DOI: 10.1109/iaw.2005.1495950

[6] Information on http: /kdd. ics. uci. edu/databases/ kddcup99.

[7] H. Zhou and C. Yang, Using Immune Algorithm to Optimize Anomaly Detection Based on SVM, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics. (2006).

DOI: 10.1109/icmlc.2006.259008

[8] Z. Xue-qin, G. Chun-hua and L. Jia-jin, Intrusion Detection System Based on Feature Selection And Support Vector Machine, Communications and Networking in China. (2006).

DOI: 10.1109/chinacom.2006.344739

[9] H. Güneş Kayacık, A. Nur Zincir-Heywood and M. I. Heywood, Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets, Proceedings of the third annual conference on privacy, security and trust. (2005).

DOI: 10.1007/11427995_29

[10] Information on http: /www. csie. ntu. edu. tw/~cjlin/libsvm.