Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection

Sunil Nilkanth Pawar; Rajankumar Sadashivrao Bichkar

doi:10.1007/s11633-014-0870-x

June 2015

Article Contents

Sunil Nilkanth Pawar and Rajankumar Sadashivrao Bichkar. Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection. International Journal of Automation and Computing, vol. 12, no. 3, pp. 337-342, 2015 doi: 10.1007/s11633-014-0870-x

Cite as: Sunil Nilkanth Pawar and Rajankumar Sadashivrao Bichkar. Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection. International Journal of Automation and Computing, vol. 12, no. 3, pp. 337-342, 2015 doi: 10.1007/s11633-014-0870-x

Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection

Sunil Nilkanth Pawar^{1
,
,},
Rajankumar Sadashivrao Bichkar²

Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra State, India;
G. H. Raisoni College of Engineering & Management, Pune, Maharashtra State, India

Received: 2013-02-15

Abstract

Genetic algorithm (GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes (VLCs) in a GA-based network intrusion detection system. Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency (DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.
- Genetic algorithms,
- intrusion detection,
- variable length chromosome,
- network security,
- evolutionary optimization

References

[1]	S. Mukkamala, J. Guadalupe, A. Sung. Intrusion detection using neural networks and support vector machines. In Proceedings of the International Joint Conference on Neural Networks, IEEE, Honolulu, HI, USA, vol. 2, pp. 1702-1707, 2002.
[2]	S. Owais, V. Snasel, P. Kromer, A. Abraham. Survey: Using genetic algorithm approach in intrusion detection systems techniques. In Proceedings of the 7th Computer Information Systems and Industrial Management Applications, IEEE, Ostrava, USA, pp. 300-307, 2008.
[3]	D. J. Day, Z. X. Zhao. Protecting against address space layout randomisation (ASLR) compromises and return-to-libc attacks using network intrusion detection systems. International Journal of Automation and Computing, vol. 8, no. 4, pp. 472-483, 2011.
[4]	M. Arun, A. Krishnan. Functional verification of signature detection architectures for high speed network applications. International Journal of Automation and Computing, vol.9, no. 4, pp. 395-402, 2012.
[5]	J. Gomez, D. Dasgupta. Evolving fuzzy classifiers for intrusion detection. In Proceedings of 2002 IEEE Workshop on Information Assurance, IEEE, West Point, NY,USA, pp. 321-323, 2002.
[6]	M. Moradi, M. Zulkernine. A neural network based system for intrusion detection and classification of attacks. In Proceedings of IEEE International Conference on Advances in Intelligent Systems-theory and Applications, IEEE, Luxembourg, Amsterdam, pp. 148-153, 2004.
[7]	R. H. Gong, M. Zulkernine, P. Abolmaesumi. A software implementation of a genetic algorithm based approach to network intrusion detection. In Proceedings of the 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and the 1st ACIS International Workshop on Self-Assembling Wireless Network, IEEE, Washington, DC, USA, pp. 246-253, 2005.
[8]	J. H. B. Ang, K. C. Tan, A. A. Mamun. A memetic evolutionary search algorithm with variable length chromosome for rule extraction. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, IEEE, Singapore, pp. 535-540, 2008.
[9]	R. Cattral, F. Oppacher, D. Deugo. Rule acquisition with a genetic algorithm. In Proceedings of the Congress on Evolutionary Computation, IEEE, Washington, DC, USA pp. 125-129, 1999.
[10]	D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning, 7th ed., Hong Kong, China: Pearson Education, pp. 1-23, 2004.
[11]	M. J. Middlemiss, G. Dick. Weighted feature extraction using a genetic algorithm for intrusion detection. In Proceedings of Congress on Evolutionary Computation, IEEE, Canberra, ACT, Australia, pp. 1669-1675, 2003.
[12]	J. L. Zhao, J. F. Zhao, J. J. Li. Intrusion detection based on clustering genetic algorithm. In Proceedings of International Conference Based on Machine Learning and Cybernetics, IEEE, Guangzhou, China, pp. 3911-3914, 2005.
[13]	T. Xiao, G. Z. Qu, S. Hariri, M. Yousif. An efficient network intrusion detection method based on information theory and genetic algorithm. In Proceedings of the 24th IEEE International Performance Computing and Communications Conference, IEEE, Phoenix, AZ, USA, pp. 11-17, 2005.
[14]	C. H. Lee, S. W. Shin, J. W. Chung. Network intrusion detection through genetic feature selection. In Proceedings of 7th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/ Distributed Computing, IEEE, Las Vegas, NV, USA, pp. 109-114, 2006.
[15]	S. Ashfaq, M. U. Farooq, A. Karim. Efficient rule generation for cost-sensitive misuse detection using genetic algorithms. In Proceedings of International Conference on Computational Intelligence and Security, IEEE, Guangzhou, China, vol. 1, pp. 282-285, 2006.
[16]	Z. M. Chen, J. Y. Feng, S. Xu, R. Z. Xu. The research of intrusion detection technology based on genetic algorithms. In Proceedings of International Conference on Networks Security, Wireless Communications and Trusted Computing, IEEE, Wuhan, China, pp. 248-250, 2009.
[17]	W. Li. A Genetic Algorithm Approach to Network Intrusion Detection, SANS Institute, USA, 2004.
[18]	R. Rajesh, M. R. Kaimal. GAVLC: GA with variable length chromosome for the simultaneous design and stability analysis of T-S fuzzy controllers. In Proceedings of IEEE International Conference on Fuzzy Systems, IEEE, Hong Kong, China, pp. 1389-1396, 2008.
[19]	B. Hutt, K. Warwick. Synapsing variable-length crossover: Meaningful crossover for variable-length genomes. IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 118-131, 2007.
[20]	S. N. Pawar, R. S. Bichkar. Using enumeration in a GAbased intrusion detection. International Journal of Computer Applications, vol. 56, no. 15, pp. 44-48, 2012.
[21]	A. Papagelis, D. Kalles. GA Tree: Genetically evolved decision trees. In Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence, IEEE, Vancouver, BC, Canada, pp. 203-206, 2000.
[22]	M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten. The WEKA data mining software: An update. In Proceedings of ACM SIGKDD Explorations Newsletter, ACM, New York, USA, vol. 11, no. 1, pp. 10-18, 2009.
[23]	W. M. Hu, J. Gao, Y. G. Wang, O. Wu, S. Maybank. Online adaboost-based parameterized methods for dynamic distributed network intrusion detection. IEEE Transactions on Cybernetics, vol. 44, no. 1, pp. 66-82, 2014.
[24]	N. N. Lu, S. G. Mabu, T. Wang, K. Hirasawa. Integrated fuzzy GNP rule mining with distance-based classification for intrusion detection system. In Proceedings of International Conference on Systems, Man, and Cybernetics, IEEE, Seoul, Korea, pp. 1569-1574, 2012.
[25]	C. Cheng, W. P. Tay, G. B. Huang. Extreme learning machines for intrusion detection. In Proceedings of the International Joint Conference on Neural Networks, IEEE, Brisbane, QLD, Australia, pp. 1-8, 2012.
[26]	H. Altwaijry. Bayesian based intrusion detection system. IAENG Transactions on Engineering Technologies, Netherlands: Springer, pp. 29-44, 2013.
[27]	Y. H. Li, J. B. Xia, S. L. Zhang, J. K. Yan, X. C. Ai, K. B. Dai. An efficient intrusion detection system based on support vector machines and gradually features removal method. Expert Systems with Applications, vol. 39, no. 1, pp. 424-430, 2012.

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Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection

Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra State, India;

G. H. Raisoni College of Engineering & Management, Pune, Maharashtra State, India

Received: 2013-02-15

Key words:

Abstract: Genetic algorithm (GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes (VLCs) in a GA-based network intrusion detection system. Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency (DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.

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Reference (27)

[1]	S. Mukkamala, J. Guadalupe, A. Sung. Intrusion detection using neural networks and support vector machines. In Proceedings of the International Joint Conference on Neural Networks, IEEE, Honolulu, HI, USA, vol. 2, pp. 1702-1707, 2002.
[2]	S. Owais, V. Snasel, P. Kromer, A. Abraham. Survey: Using genetic algorithm approach in intrusion detection systems techniques. In Proceedings of the 7th Computer Information Systems and Industrial Management Applications, IEEE, Ostrava, USA, pp. 300-307, 2008.
[3]	D. J. Day, Z. X. Zhao. Protecting against address space layout randomisation (ASLR) compromises and return-to-libc attacks using network intrusion detection systems. International Journal of Automation and Computing, vol. 8, no. 4, pp. 472-483, 2011.
[4]	M. Arun, A. Krishnan. Functional verification of signature detection architectures for high speed network applications. International Journal of Automation and Computing, vol.9, no. 4, pp. 395-402, 2012.
[5]	J. Gomez, D. Dasgupta. Evolving fuzzy classifiers for intrusion detection. In Proceedings of 2002 IEEE Workshop on Information Assurance, IEEE, West Point, NY,USA, pp. 321-323, 2002.
[6]	M. Moradi, M. Zulkernine. A neural network based system for intrusion detection and classification of attacks. In Proceedings of IEEE International Conference on Advances in Intelligent Systems-theory and Applications, IEEE, Luxembourg, Amsterdam, pp. 148-153, 2004.
[7]	R. H. Gong, M. Zulkernine, P. Abolmaesumi. A software implementation of a genetic algorithm based approach to network intrusion detection. In Proceedings of the 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and the 1st ACIS International Workshop on Self-Assembling Wireless Network, IEEE, Washington, DC, USA, pp. 246-253, 2005.
[8]	J. H. B. Ang, K. C. Tan, A. A. Mamun. A memetic evolutionary search algorithm with variable length chromosome for rule extraction. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, IEEE, Singapore, pp. 535-540, 2008.
[9]	R. Cattral, F. Oppacher, D. Deugo. Rule acquisition with a genetic algorithm. In Proceedings of the Congress on Evolutionary Computation, IEEE, Washington, DC, USA pp. 125-129, 1999.
[10]	D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning, 7th ed., Hong Kong, China: Pearson Education, pp. 1-23, 2004.
[11]	M. J. Middlemiss, G. Dick. Weighted feature extraction using a genetic algorithm for intrusion detection. In Proceedings of Congress on Evolutionary Computation, IEEE, Canberra, ACT, Australia, pp. 1669-1675, 2003.
[12]	J. L. Zhao, J. F. Zhao, J. J. Li. Intrusion detection based on clustering genetic algorithm. In Proceedings of International Conference Based on Machine Learning and Cybernetics, IEEE, Guangzhou, China, pp. 3911-3914, 2005.
[13]	T. Xiao, G. Z. Qu, S. Hariri, M. Yousif. An efficient network intrusion detection method based on information theory and genetic algorithm. In Proceedings of the 24th IEEE International Performance Computing and Communications Conference, IEEE, Phoenix, AZ, USA, pp. 11-17, 2005.
[14]	C. H. Lee, S. W. Shin, J. W. Chung. Network intrusion detection through genetic feature selection. In Proceedings of 7th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/ Distributed Computing, IEEE, Las Vegas, NV, USA, pp. 109-114, 2006.
[15]	S. Ashfaq, M. U. Farooq, A. Karim. Efficient rule generation for cost-sensitive misuse detection using genetic algorithms. In Proceedings of International Conference on Computational Intelligence and Security, IEEE, Guangzhou, China, vol. 1, pp. 282-285, 2006.
[16]	Z. M. Chen, J. Y. Feng, S. Xu, R. Z. Xu. The research of intrusion detection technology based on genetic algorithms. In Proceedings of International Conference on Networks Security, Wireless Communications and Trusted Computing, IEEE, Wuhan, China, pp. 248-250, 2009.
[17]	W. Li. A Genetic Algorithm Approach to Network Intrusion Detection, SANS Institute, USA, 2004.
[18]	R. Rajesh, M. R. Kaimal. GAVLC: GA with variable length chromosome for the simultaneous design and stability analysis of T-S fuzzy controllers. In Proceedings of IEEE International Conference on Fuzzy Systems, IEEE, Hong Kong, China, pp. 1389-1396, 2008.
[19]	B. Hutt, K. Warwick. Synapsing variable-length crossover: Meaningful crossover for variable-length genomes. IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 118-131, 2007.
[20]	S. N. Pawar, R. S. Bichkar. Using enumeration in a GAbased intrusion detection. International Journal of Computer Applications, vol. 56, no. 15, pp. 44-48, 2012.
[21]	A. Papagelis, D. Kalles. GA Tree: Genetically evolved decision trees. In Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence, IEEE, Vancouver, BC, Canada, pp. 203-206, 2000.
[22]	M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten. The WEKA data mining software: An update. In Proceedings of ACM SIGKDD Explorations Newsletter, ACM, New York, USA, vol. 11, no. 1, pp. 10-18, 2009.
[23]	W. M. Hu, J. Gao, Y. G. Wang, O. Wu, S. Maybank. Online adaboost-based parameterized methods for dynamic distributed network intrusion detection. IEEE Transactions on Cybernetics, vol. 44, no. 1, pp. 66-82, 2014.
[24]	N. N. Lu, S. G. Mabu, T. Wang, K. Hirasawa. Integrated fuzzy GNP rule mining with distance-based classification for intrusion detection system. In Proceedings of International Conference on Systems, Man, and Cybernetics, IEEE, Seoul, Korea, pp. 1569-1574, 2012.
[25]	C. Cheng, W. P. Tay, G. B. Huang. Extreme learning machines for intrusion detection. In Proceedings of the International Joint Conference on Neural Networks, IEEE, Brisbane, QLD, Australia, pp. 1-8, 2012.
[26]	H. Altwaijry. Bayesian based intrusion detection system. IAENG Transactions on Engineering Technologies, Netherlands: Springer, pp. 29-44, 2013.
[27]	Y. H. Li, J. B. Xia, S. L. Zhang, J. K. Yan, X. C. Ai, K. B. Dai. An efficient intrusion detection system based on support vector machines and gradually features removal method. Expert Systems with Applications, vol. 39, no. 1, pp. 424-430, 2012.

Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection

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