A Novel Approach of Detector Generation for Real-Valued Negative Selection Algorithm

Abstract:

The detector sets generated by Real-Valued Negative Selection Algorithm (RNSA) are usually numerous, without optimization, and can not work under real-time condition. Thus, a novel approach of detector generation for RNSA based on Clonal Selection and Neighborhood Search (CSNS-RNSA) is proposed. Clonal selection of the immune mechanism is introduced to implement global search in a quasi-random sequence. The Gaussian mutation operator is proposed to get the global optimal detection sets of N-dimensional space through Neighborhood search. The resulting detector sets achieved a good coverage of non-self space, and also significantly reduced the number of detector sets, thus overcome the limitations of original RNSA. Finally, experiments verify the effectiveness of the algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 3736-3740
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.3736
• Online since: October 2011
• Authors: Rong Hua Hu, Pei Huang Lou, Peng Zhao
• Keywords: Clonal Selection, Detector Generation, Negative Selection Algorithm, Neighborhood Search

References

[1] D. Dasgupta and N. Attoh-Okine, Immunity-based systems: A survey, in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, October 1997, pp.369-374.

DOI: 10.1109/icsmc.1997.625778

[2] L. N. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, London, UK: Springer-Verlag, (2002).

[3] Forrest S, Perelson AS. Self-nonself discrimination in a computer. In: Proceedings of IEEE symposium on security and privacy, Oakland; 1994. p.202–213.

DOI: 10.1109/risp.1994.296580

[4] D. Dasgupta and F. Gonzalez. An immunity-based technique to characterize intrusion in computer networks. IEEE Transactions on Evolutionary Computation, 2002, 6(3), p.1081–1088.

DOI: 10.1109/tevc.2002.1011541

[5] F. A. Gonzalez and D. Dasgupta. Anomaly detection using real-valued negative selection. Genetic Programming and Evolvable Machines, 2003, 4, p: 383–403.

[6] Harmer PK, Williams PD, Gunsch GH, et al. An artificial immune system architecture for computer security applications. IEEE Trans Evol Comput 2002; 6(3), p.252.

DOI: 10.1109/tevc.2002.1011540

[7] Zhou Ji and Dasgupta D. Augmented negative selection algorithm with variable-coverage detectors. In Proceedings of 2004 Congress on Evolutionary Computation, Portland, OR, USA: CEC, 2004, pp.1081-1088.

DOI: 10.1109/cec.2004.1330982

[8] Zhou Ji and Dasgupta D. Real-valued negative selection algorithm with variable-sized detectors . In Proceedings of 2004 Genetic and Evolutionary Computation Conference, Seattle, WA, USA: GECCO, 2004, p.287–298.

DOI: 10.1007/978-3-540-24854-5_30

[9] Zhou Ji and Dasgupta D. V-Detector : An Efficient Negative Selection Algorithm with Probably Adequate, Detector Coverage, Information Sciences, 2009, 179, pp.1390-1406.

DOI: 10.1016/j.ins.2008.12.015

[10] Jorge L, Amaral M and José F A, Ricardo T. Real-Valued negative selection algorithm with a Quasi-Monte Carlo genetic detector generation. In Proceedings of the 6th International Conference on Artificial Immune System, Barcelona, Spain: ICARIS, 2007, p.156.

DOI: 10.1007/978-3-540-73922-7_14

[11] de Castro, L. N. and Von Zuben, F. J. : The Clonal Selection Algorithm with Engineering Applications. Proc. of GECCO'00, Workshop on Artificial Immune Systems and Their Applications, 2000, pp.36-37.