An introduction to differential evolution
New ideas in optimization
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
An approach to implement a network intrusion detection system using genetic algorithms
SAICSIT '04 Proceedings of the 2004 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
Optimization of Intrusion Detection through Fast Hybrid Feature Selection
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
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Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. In this paper, we investigate the use of evolution algorithms for features selection approach in IDS. We compared the performance of three feature selection algorithms: Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Differential Evolution (DE) using KDD Cup 1999 dataset. Our results show that DE is clearly and consistently superior compared to GAs and PSO for feature selection problems, both in respect to classification accuracy as well as number of features.