The week's pick
Reseach Article
Intrusion Detection Model based on Differential Evolution
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 36 - Number 6 |
| Year of Publication: 2011 |
| Authors: M. Sailaja, R. Kiran Kumar, P. Sita Rama Murty |
10.5120/4494-6328
|
M. Sailaja, R. Kiran Kumar, P. Sita Rama Murty . Intrusion Detection Model based on Differential Evolution. International Journal of Computer Applications. 36, 6 ( December 2011), 10-13. DOI=10.5120/4494-6328
Abstract
Information systems need to be constantly monitored and audited; analysis of security event logs in heavy traffic networks is a challenging task. In this paper we considered Differential Evolution for the intrusion detection problem. We used NSL_KDD dataset for our experiments which is derived from the standard KDD CUP 99 Intrusion Dataset. We also provided the comparative results of the differential evolution with the state of the art classification algorithm like SVM. We reduced the dimension/features of the NSK_KDD datasets using rough set algorithm and ran DE and SVM this increased the speed of the evolutionary algorithm without compromising the detection rate.
References
- R Storn and K Price, Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization 11: 341-359, 1997.
- Xiaobu Liu, Chao Yu, and Zhihua Cai., Differential Evolution Based Band Selection in Hyperspectral Data Classification, ISICA 2010, LNCS 6382, pp. 86-94, 2010.
- Mahbood Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani., A Detailed Analysis of the KDD CUP 99 Data set, Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009).
- H. Jin, J. Sun, H. Chen, and Z. Han., A Fuzzy Data Mining Based Intrusion Detection System, Proceedings of 10thInternational Workshop on future Trends in Distributed Computing Systems (FTDCS04) IEEE Computer Society, Suzhou, China, May 26-28, 2004, 191-197.
- Surat Srinoy., Intrusion Detection Model Based on Particle Swarm Optimization and Support Vector Machine, Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2007), 186-192.
- H. T. Ptacek and N. T. Newsham., Insertion, Evasion and Denial of Service: Eluding Network Intrusion Detection; Secure Networks, Inc., January 1998.
- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
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