Network intrusion detection system using genetic network programming with support vector machine
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Intrusion Detection Systems (IDS) are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively in effective. Recently applying Artificial Intelligence, machine learning and data mining techniques to IDS are increasing. Artificial Intelligence plays a driving role in security services. An intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented in this paper. The novel structure model has higher accuracy and faster convergence speed. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. Utilizing the character that rough set can keep the discernability of original dataset after reduction, the reduces of the original dataset are calculated and used to train neural network, which increase the detection accuracy. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.