INTRUSION DETECTION SYSTEMS USING ADAPTIVE REGRESSION SPLINES

Srinivas Mukkamala, Andrew H. Sung, Ajith Abraham, Vitorino Ramos

Abstract

Past 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.

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Paper Citation


in Harvard Style

Mukkamala S., H. Sung A., Abraham A. and Ramos V. (2004). INTRUSION DETECTION SYSTEMS USING ADAPTIVE REGRESSION SPLINES . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 972-8865-00-7, pages 26-33. DOI: 10.5220/0002649600260033


in Bibtex Style

@conference{iceis04,
author={Srinivas Mukkamala and Andrew H. Sung and Ajith Abraham and Vitorino Ramos},
title={INTRUSION DETECTION SYSTEMS USING ADAPTIVE REGRESSION SPLINES},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2004},
pages={26-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002649600260033},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - INTRUSION DETECTION SYSTEMS USING ADAPTIVE REGRESSION SPLINES
SN - 972-8865-00-7
AU - Mukkamala S.
AU - H. Sung A.
AU - Abraham A.
AU - Ramos V.
PY - 2004
SP - 26
EP - 33
DO - 10.5220/0002649600260033