International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 2, No. 2, 8 Dec. 2010
Cover page and Table of Contents: PDF (size: 331KB)
A new Immunity Intrusion Detection Model Based on Genetic Algorithm and Vaccine Mechanism
Full Text (PDF, 331KB), PP.33-39
Views: 0 Downloads: 0
Author(s)
Jing Xiao-Pei
1,*
Wang Hou-Xiang
1
Index Terms
Intrusion detection, genetic algorithm, vaccine mechanism, feature extraction
Abstract
After analyzing the characteristics of Immunity Intrusion Detection System, by utilizing prominent characteristics of genetic algorithm and vaccine mechanism, a new hybird immunity intrusion detection model based on genetic algorithm and vaccine mechanism was established. The modeling process is described in detail, such as feature extraction of vaccine, genetic operates to memory detectors and the improvement for detection method. Via application vaccine mechanism into intrusion detection system, the new model has the function of misuse detection and anomaly detection simultaneously. In order to improve the detection matching efficiency, we also present a novel matching algorithm RBNDM. Finally, we evaluated our model using the KDD Cup 1999 Data set. The experiments show that this model can increase the true positive rate of the IDS.
Cite This Paper
Jing Xiao-Pei, Wang Hou-Xiang, "A new Immunity Intrusion Detection Model Based on Genetic Algorithm and Vaccine Mechanism", International Journal of Computer Network and Information Security(IJCNIS), vol.2, no.2, pp.33-39, 2010. DOI:10.5815/ijcnis.2010.02.05
Reference
[1] Hofmeyr, S, Forrest, S. “Immunity by Design: An Artificial Immune System,” Proceedings of the 1999 Genetic and Evolutionary Computation Conference, 1999, pp. 1289-1296.
[2] Jiao Li-cheng, Du Hai-feng, et al. “Immune Optimization Computing, Learning and Identification,” Bei Jing: Publishing House of Science, 2006, pp. 89-96.
[3] Jungwon Kim, Peter J. Bentley. “Towards an Artificial Immune System for Network Intrusion Detection: An Investigation of Dynamic Clonal Selection,” wcci, 2002, vol. 2, pp.1015-1020.
[4] Jinyin Chen, Dongyong Yang, et al. “A Study of Detector Generation Algorithms Based on Artificial Immune in Intrusion Detection System,” WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE, 2007, vol. 4 (3), pp.29-35.
[5] Hunt J E, Cooke D E. “Learning Using an Artificial Immune System,” Journal of network and computer applications, 1996, vol. 19, pp. 189-212.
[6] Cantu-Paz E. “Feature subset selection, class separability, and genetic algorithms,” Proceedings of the Genetic and Evolutionary Computation Conf, 2004, pp. 959-970.
[7] Kop M, Liu Xiu-fen. “Texture Detection by Genetic Programming,” Proceedings of Congress on Evolutionary Computation, 2001, pp. 867-872.
[8] Hou Hai-yu, Gerry Dozier. “Immunity-Based Intrusion Detection System Design, Vulnerability Analysis, and GENERTIA’s Genetic Arms Race,” ACM Symposium on Applied Computing, 2005, pp. 952-956.
[9] Jungwon Kim, Peter J. Bentley. “An Evaluation of Negative Selection in an Artificial Immune System for Network Intrusion Detection,” In GECC0-2001 Proceedings, 2001, pp. 1330–1337.
[10] Hofneyr S A, Forrest S. “Architecture for an artificial immune system,” Evolutionary Computation, 2000, vol. 8(4), pp. 443-473.
[11] Chen You, Cheng Xue-Qi, Li Yang, et al. “Lightweight Intrusion Detection System Based on Feature Selection,” Journal of Software, 2007, vol. 18(7), pp. 1639-1651.
[12] Jing Xiao-pei, Wang Hou-xiang, Han Ruo-fei, et al. “Improved Genetic Algorithm in Intrusion Detection Model Based on Artificial Immune Theory,” Proceedings of the CNMT 2009, 2009, pp. 659-662.
[13] Yan Xuan-hui. “An Artificial Immune-Based Intrusion Detection Model Using Vaccination Strategy,” Acta Electronica Sinica, 2009, vol. 37(4), pp. 780-785.
[14] Yu Y, Huang H. “An ensemble approach to intrusion detection based on improved multi-objective genetic Algorithm,” Journal of Software, 2007, vol. 18(6), pp. 1369-1378.
[15] Gonzalo Navarro, Mathieu Raffinot. “Flexible Pattern Matching in String,” Bei Jing: Publishing House of Electronics Industry, 2007, pp. 26-29.
[16] Branislav Durian, Jan Holub, Hannu Peltola, Jorma Tarhio. “Tuning BNDM with q-Grams,” In proc. of Tength Workshops on Algorithm Engineering and Experiments, 2009, pp. 29-37.
[17] KDD cup 1999 data. http://kdd.ics.uci.edu/databases/ kddcup99/ kddcup99.html.
[18] Wang Jie-song, Zhang Xiao-fei. “The Analysis and Preprocess for Network Intrusion Detection data KDDCup99,” Science & Technology Information, 2008. 15, pp. 407-408.