Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Modelling intrusion detection as an allocation problem
Pattern Recognition Letters
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An ensemble-based evolutionary framework for coping with distributed intrusion detection
Genetic Programming and Evolvable Machines
An effective intrusion detection method using optimal hybrid model of classifiers
Journal of Computational Methods in Sciences and Engineering - Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2009
GP ensemble for distributed intrusion detection systems
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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This paper investigates the suitability of linear genetic programming (LGP) technique to model efficient intrusion detection systems, while comparing its performance with artificial neural networks and support vector machines. Due to increasing incidents of cyber attacks and, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. We also investigate key feature indentification for building efficient and effective IDSs. Through a variety of comparative experiments, it is found that, with appropriately chosen population size, program size, crossover rate and mutation rate, linear genetic programs could outperform support vector machines and neural networks in terms of detection accuracy. Using key features gives notable performance in terms of detection accuracies. However the difference in accuracy tends to be small in a few cases.