Intrusion Detection Systems Using Adaptive Regression Splines

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dc.creatorMukkamala, Srinivas
dc.creatorSung, Andrew H.
dc.creatorAbraham, Ajith
dc.creatorRamos, Vitorino
dc.date2004-05-05
dc.date.accessioned2026-07-07T03:21:12Z
dc.date.available2026-07-07T03:21:12Z
dc.descriptionPast few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDS) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.
dc.identifierhttps://arxiv.org/abs/cs/0405016
dc.identifierhttp://arxiv.org/abs/cs/0405016
dc.identifier6th International Conference on Enterprise Information Systems, ICEIS'04, Portugal, I. Seruca, J. Filipe, S. Hammoudi and J. Cordeiro (Eds.), ISBN 972-8865-00-7, Vol. 3, pp. 26-33, 2004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32105
dc.subjectArtificial Intelligence
dc.subjectC.2.0
dc.titleIntrusion Detection Systems Using Adaptive Regression Splines
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