Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30579
Combine a Population-based Incremental Learning with Artificial Immune System for Intrusion Detection System

Authors: Jheng-Long Wu, Pei-Chann Chang, Hsuan-Ming Chen

Abstract:

This research focus on the intrusion detection system (IDS) development which using artificial immune system (AIS) with population based incremental learning (PBIL). AIS have powerful distinguished capability to extirpate antigen when the antigen intrude into human body. The PBIL is based on past learning experience to adjust new learning. Therefore we propose an intrusion detection system call PBIL-AIS which combine two approaches of PBIL and AIS to evolution computing. In AIS part we design three mechanisms such as clonal selection, negative selection and antibody level to intensify AIS performance. In experimental result, our PBIL-AIS IDS can capture high accuracy when an intrusion connection attacks.

Keywords: Intrusion Detection, artificial immune system, population-based incremental learning, evolution computing

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072796

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1539

References:


[1] L. M. R. Baccarini, V. V. R. Silva, B. R. Menezes, and W. M. Caminhas, "SVM practical industrial application for mechanical faults diagnostic," Expert Systems with Applications, vol. 38 no. 6, pp. 6980-6984, 2011.
[2] Z. E. Gketsis, M. E. Zervakis, and G. Stavrakakis, "Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN," Electric Power Systems Research, vol. 79 no. 11, pp. 1483-1494, 2009.
[3] R. Razavi-Far, H. Davilu, V. Palade, and C. Lucas, "Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks," Neurocomputing, vol. 72 no. 13-15, pp. 2939-2951, 2009.
[4] D. Srinivasan, R. L. Cheu, Y. P. Poh, and A. k. C. Ng,"Automated fault detection in power distribution networks using a hybrid fuzzy-genetic algorithm approach," Engineering Applications of Artificial Intelligence, vol. 13 no. 4, pp. 407-418, 2000.
[5] B. Chandra Mohan, and R. Baskaran, "A survey: Ant Colony Optimization based recent research and implementation on several engineering domain," Expert Systems with Applications, vol. 39 no. 4, pp. 4618-4627, 2012.
[6] M. Maitra, and A. Chatterjee, "A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding," Expert Systems with Applications, vol. 34 no. 2, pp. 1341-1350, 2008.
[7] S. Nemati, M. E. Basiri, N. Ghasem-Aghaee, and A. M. Aghdam, "A novel ACO-GA hybrid algorithm for feature selection in protein function prediction," Expert Systems with Applications, vol. 36 no. 10, pp. 12086-12094, 2009.
[8] H. Zhao, "A multi-objective genetic programming approach to developing Pareto optimal decision trees," Decision Support Systems, vol. 43 no. 3, pp. 809-826, 2007.
[9] I. Aydin, M. Karakose, and E. Akin, "A multi-objective artificial immune algorithm for parameter optimization in support vector machine," Applied Soft Computing, vol. 11 no. 1, pp. 120-129, 2011.
[10] D. J. Shin, J. O. Kim, T. K. Kim, J. B. Choo, and C. Singh, "Optimal service restoration and reconfiguration of network using Genetic-Tabu algorithm," Electric Power Systems Research, vol. 71 no. 2, pp. 145-152, 2004.
[11] D. Dasgupta, S. Yu, and F. Nino, "Recent Advances in Artificial Immune Systems-Models and Applications," Applied Soft Computing, vol. 11 no. 2, pp. 1574-1587, 2011.
[12] H. J. Mattord, Principles of Information Security. Course Technology Florence, 2008 pp. 290-301.
[13] M. S. Abadeh, H. M., and J. Habibi, "Design and analysis of genetic fuzzy systems for intrusion detection in computer networks," Expert Systems with Applications, vol. 38, pp. 7067-7075, 2011.
[14] H. Altwaijry, and S. Algarny, "Bayesian based intrusion detection system," Journal of King Saud University - Computer and Information Sciences, article in press, 2011.
[15] S. J. Horng, M. Y. Su, Y. H. Chen, T. W. Kao, R. J. Chen, J. L. Lai, C. D. Perkasa, "A novel intrusion detection system based on hierarchical clustering and support vector machines" Expert Systems with Applications, vol. 38, pp. 306-313, 2011.
[16] L. N. de Castro, and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach. Springer. New York, 2002 pp. 57-58.
[17] E. Hart and J. Timmis, "Application areas of AIS: The past, the present and the future," Applied Soft Computing, vol. 8, no. 1, pp. 191-201, 2008.
[18] S. Darmoul, H. Pierreval, and S.H. Gabouj, "Scheduling using artificial immune system metaphors: A review," in Proceedings of IEEE Conference on Service Systems and Service Management, pp. 1150-1155, 2006.
[19] B. Alatas, and E. Akin, "Mining fuzzy classification rules using an artificial immune system with boosting," Advances in Databases and Information Systems, vol. 3631, pp. 283-293, 2005.
[20] F. Gonzalez and D. Dasgupta, "Artificial Immune Systems Research in the Last Five Years," in Proceedings of the Congress on Evolutionary Computation Conference, Canberra, pp. 8-12, 2003.
[21] R. Tavakkoli-Moghaddam, A. R. Rahimi-Vahed, and A. H. Mirzaei, "Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm," International Journal of Advanced Manufacturing Technology, vol. 36, no. 9-10, pp. 969-981, 2008.
[22] C. H. Liu, P. C. Chang, and Y. W. Wang, "Two-stage Artificial Immune System in Grid Scheduling Problems," in Proceeding 5th International Conference on Computer Sciences and Convergence Information Technology, pp. 822-827, Nov. 2010.
[23] S. Baluja and R. Caruana, "Removing the Genetics from the Standard Genetic Algorithm," in Proceeding 12th International Conference on Machine Learning, pp. 38-46. 1995.
[24] F. O. Karray, and C. de Silva, Soft computing and intelligent systems design. Addison Wesley, 2004.
[25] R. Rastegar, and A. Hariri, "The Population-Based Incremental Learning Algorithm converges to local optima," Neurocomputing, vol. 69 no. 13-15, pp. 1772-1775, 2006.
[26] K. A. Folly, "Power System Stabilizer Design for Multimachine Power System Using Population-Based Incremental Learning," Power Plants and Power Systems Control , pp. 41-46, 2007.
[27] UCI Machine Learning Repository (Online), Available: http://www.ics.uci.edu/~mlearn/MLRepository.html.