Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
802.11 de-authentication attack detection using genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
The feature selection and intrusion detection problems
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Review: Intrusion detection system: A comprehensive review
Journal of Network and Computer Applications
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Due to increasing incidents of cyber attacks and heightened concerns for cyber terrorism, implementing effective intrusion detection systems (IDSs) is an essential task for protecting cyber security--as well as physical security because of the great dependence on networkedcomputers for the operational control of various infrastructures.Building effective IDSs, unfortunately, has remained an elusive goal owing to the great technical challenges involved; and applied AI techniques are increasingly being utilized in attempts to overcome the difficulties. This paper presents a comparative study of using support vector machines (SVMs), artificial neural networks (ANNs), multivariate adaptive regression splines (MARS) and linear genetic programs (LGPs) for intrusion detection. We investigate and compare the performance of IDSs based on the mentioned techniques, with respect to a well-known set of intrusion evaluation data gathered by Lincoln Labs.Through a variety of experiments and analysis, it is found that, with appropriately chosen population size, program size, crossover rate and mutation rate, LGPs outperform other techniques in terms of detection accuracy at the expense of time. SVMs outperform MARSand ANNs in three critical aspects of intrusion detection: accuracy, training time, and testing time.