Research of Intrusion Detection Based on Immune Algorithm

Abstract:

On the basic of multiple populations of immune algorithm and clonal selectionalgorithm, the purpose of this paper is to further improve the detectionefficiency and reducing the false alarm rate. This paper uses the kddcup99 dataset as the experimental data set, and chooses four types of attack data groupof experiment data set as initial population of multiple populations of clonalselection algorithm, through the algorithm to create the optimal model. Basedon the principle of normal data larger than the abnormal data, in turn,experimental data set matched with the normal data set and the optimal model bythe improved R matching algorithm. The results of this paper show that thedetection rate increased significantly

• Periodical: Advanced Materials Research (Volumes 1079-1080)
• Pages: 747-751
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1079-1080.747
• Online since: December 2014
• Authors: Ming Jun Wei*, Yue Yue Wang, Jian Guo Jin
• Keywords: Detection Rate, Intrusion Detection, Multi-Colony Immune Algorithm, R Matching Rules

References

[1] Kongyu Yang: Immune evolutionary theory and application (Social sciences academic press, Beijing 2008).

[2] Keming Xie, Hongbo Guo: Overview of Artificial Immune System and Algorithm (Computer Engineering and Applications, China 2005).

[3] FORREST S, PERELSON A and ALLEN L: Self-nonself discrimination in a computer (Proc. of IEEE Symposium on Research in Security and Privacy. Oakland, CA, 1994).

DOI: 10.1109/risp.1994.296580

[4] De Castro L N, Van Zuben F: The clonal selection algorithm with engineering applications (Morgan Kaufman Publishers, USA 2000).

[5] Kim J, Bentley P J. Towards: An artificial immune system for network intrusion; An investigation of dynamic clonal selection (IEEE Press, Washington D.C. 2002).

[6] KENG Qichao, ZHANG Qinghua: The research progress of network intrusion detection algorithm based on artificial immune (Journal of Kwangtung institute of petroleum and chemical, China 2012).

[7] Xingbao Liu, Zixing Cai: Hybrid immune evolutionary algorithm for global optimization problems (Journal of XIDIAN University, China 2010).

[8] Jianjun Yu, Shudong Sun: Four improved immune algorithm and its comparison (Systems engineering, China 2006).

[9] Xin Ma, Qin Li: Research and implementation of clonal selection algorithm (Reform and opening, China 2010).

[10] Liu Y, Chen K and Liao X: A genetic clustering method for Intrusion Detection (Pattern Recognition, China 2004).