Abstract
Recently, considerable research work have been conducted towards finding fast and accurate pattern classifiers for training Intrusion Detection Systems (IDSs). This paper proposes using the so called Fuzzy ARTMAT classifier to detect intrusions in computer network. Our investigation shows, through simulations, how efficient such a classifier can be when used as the learning mechanism of a typical IDS. The promising evaluation results in terms of both detection accuracy and training duration indicate that the Fuzzy ARTMAP is indeed viable for this sort of application.
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Araújo, N., de Oliveira, R., Ferreira, E.W.T., Nascimento, V., Akira, A.S., Bhargava, B. (2012). Performance Evaluation of the Fuzzy ARTMAP for Network Intrusion Detection. In: Thampi, S.M., Zomaya, A.Y., Strufe, T., Alcaraz Calero, J.M., Thomas, T. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2012. Communications in Computer and Information Science, vol 335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34135-9_3
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DOI: https://doi.org/10.1007/978-3-642-34135-9_3
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