Random Articles
Reseach Article
Using Adaptive Mutation in a GA based Intrusion Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 73 - Number 3 |
| Year of Publication: 2013 |
| Authors: S. N. Pawar, R. S. Bichkar |
10.5120/12724-9561
|
S. N. Pawar, R. S. Bichkar . Using Adaptive Mutation in a GA based Intrusion Detection. International Journal of Computer Applications. 73, 3 ( July 2013), 39-43. DOI=10.5120/12724-9561
Abstract
This paper uses a GA-based approach for intrusion detection and uses an adaptive mutation to improve its performance. The drawback of conventional GA is its randomness of mutation which is applied to all the chromosomes irrespective of their fitness. Thus a very good chromosome is equally likely to be disrupted by mutation as a bad one. On the other hand bad chromosomes are less likely to produce good ones through crossover if they are not changed. Hence, it is proposed to use fitness proportionate adaptive mutation in a GA based intrusion detection. This adaptive mutation function does not change the fittest chromosome and causes a change in the low fit chromosomes. This causes the genetic algorithm to arrive at better solution. Experimental results show that this technique improves the fitness of the classification rules and in turn increases the intrusion detection rate.
References
- A. Vesely, D. Brechlerova, "Neural Networks in Intrusion Detection Systems", AGRIC. ECON. CZECH, 50, 2004 (1):35-39.
- S. Owais, V. Snasel and P. Kromer, A. Abraham, "Survey Using Genetic Algorithm Approach in Intrusion Detection Systems Techniques", 7th Computer Information Systems and Industrial Management Applications, 2008, IEEE press, June2008, pp. 300-307.
- Yong Wang, DawuGu, XiuxiaTian, Jing Li, "Genetic Algorithm Rule Definition for Denial of Services Network Intrusion Detection", International Conference on Computational Intelligence and Natural Computing, IEEE, 2009, pp. 99-102.
- RenHui Gong, Mohammad Zulkernine, PurangAbolmaesumi, "A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection", SNPD/SAWN'05, IEEE, 2005.
- Biswanath Mukherjee, L. ToddHerbe- rlein and Karl N. Levitt, "Network Intrusion Detection", IEEE Network, 8(3):26-41, May/June 1994.
- S. MarsiliLibelli, P. Alba, "Adaptive Mutation in Genetic Algorithms", Soft Computing 4 (2000), Springer- Verlog, 2000, pp. 76-80.
- XuHaiyan, "Research for New Modified Adaptive Genetic Algorithm," World Automation Congress, IEEE, 2012,pp. 1- 4.
- Dirk Thierens, "Adaptive MutationRate Control Schemes in Genetic Algorithms", IEEE, 2002,pp. 980-985.
- C. W. Ho, K. H. Lee and K. S. Leung, "A Genetic Algorithm Based on Mutation and Crossover with Adaptive Probabilities" IEEE,1999, pp. 768-785.
- FatemehVafaee and Peter C. Nelson , "A Genetic Algorithm That Incorporates an Adaptive Mutation Based On an Evolutionary Model", International Conference on Machine Learning and Applications,IEEE,2009,pp. 101-107.
- Zhiming Liu, Jiliu Zhou, Su Lai, "New Adaptive Genetic Algorithm Based on Ranking," Proceedings of the Second International Conference on Machine Learning and Cybernetics, X. 'an, 2-5 November 2003. IEEE, 2003, pp. 1841-1844.
- ChiHoon Lee, Sung Woo Shin and Jin Wook Chung, "Network Intrusion Detection Through Genetic Feature Selection", SNPD, IEEE, 2006.
- Nalini N. and RaghavendraRao G. , "Network Intrusion Detection via a Hybrid of Genetic Algorithms and Principal Component Analysis", IEEE, 2006.
- Hua Zhou, Xingu Meng and Li Zhang, "Application of Support Vector Machine and Genetic Algorithm to Network Intrusion Detection", IEEE, 2007.
- Chen Zhongmin, FengJianyuan, Xu Sheng, XuRenzuo, "The Research of Intrusion Detection Technology Based on Genetic Algorithms," International Conference on Networks Security, Wireless Communications and Trusted Computing, IEEE, 2009.
- Sinclair Chris, L. Pierce, and S. Matzner, "An Application of Machine Learning to Network Intrusion Detection", Annual Computer Security Applications Conference, 1999, pp. 371-377.
- S. N. Pawar and R. S. Bichkar, " UsingEnume-ration in a GA based Intrusion Detection", International Journal of Computer Applications (IJCA), October, 2012.
- MIT Lincoln Laboratory, DARPA datasets,MIT,USA,http://www. ll. mit. edu/mission/communications/ist/corpora/ideval/data/1998data. html
Index Terms
Keywords