Combine a Population-based Incremental Learning with Artificial Immune System for Intrusion Detection System
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:58521", author = "Jheng-Long Wu and Pei-Chann Chang and Hsuan-Ming Chen", title = "Combine a Population-based Incremental Learning with Artificial Immune System for Intrusion Detection System", 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 = "Artificial immune system, intrusion detection,
population-based incremental learning, evolution computing.", volume = "6", number = "5", pages = "635-6", }
