Improving intrusion detection performance using keyword selection and neural networks
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ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
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IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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International Journal of Advanced Intelligence Paradigms
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High learning and adaptation ability of intelligent agents based on artificial neural networks (ANNs) has made them a popular tool in design and implementation of intrusion detection systems (IDS). However, ANN might consume significant resources during their retraining because of network changes. The paper investigates the design of ANN structures that may reduce the resource consumption without a substantial performance degradation. It describes the results of empirical studies examining a variety of design solutions, such as the choice of the ANN architecture and its parameters, the choice of an ANN fully connected topology versus a partial connectivity and the IDS design in a form of a hierarchical system of heterogeneous ANN-based agents. The results are analysed and design recommendations are provided. The fully connected ANN structure optimised with genetic algorithms has been found to achieve the best performance, while partial connectivity might save resources without a significant sacrifice of possible accomplishments.