Article

Comparison of BPL and RBF network in intrusion detection system

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RSFDGrC'03: Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing

Pages 466 - 470

Published: 26 May 2003 Publication History

Abstract

In this paper, we present the performance comparison results of the backpropagation learning (BPL) algorithm in a multilayer perceptron (MLP) neural network and the radial basis functions (RBF) network for intrusion detection. The results show that RBF network improves the performance of intrusion detection systems (IDSs) in anomaly detection with a high detection rate and a low false positive rate. RBF network requires less training time and can be optimized to balance the detection and the false positive rates.

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Cited By

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Masarat SSharifian STaheri H(2016)Modified parallel random forest for intrusion detection systemsThe Journal of Supercomputing10.1007/s11227-016-1727-672:6(2235-2258)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s11227-016-1727-6
Kirubavathi Venkatesh GAnitha Nadarajan R(2012)HTTP botnet detection using adaptive learning rate multilayer feed-forward neural networkProceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems10.1007/978-3-642-30955-7_5(38-48)Online publication date: 20-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-30955-7_5
Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019

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Published In

RSFDGrC'03: Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing

May 2003

740 pages

ISBN:3540140409

Editors:
Guoyin Wang
Chongqing University of Posts and Telecommunications, Institute of Computer Science and Technology, Chongqing, P.R. China
,
Qing Liu
Nanchang University, Department of Computer Science, Nanchang, P.R. China
,
Yiyu Yao
University of Regina, Department of Computer Science, Regina, Saskatchewan, Canada
,
Andrzej Skowron
Warsaw University, Institute of Mathematics, 097Warsaw, Poland

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Springer-Verlag

Berlin, Heidelberg

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Published: 26 May 2003

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Masarat SSharifian STaheri H(2016)Modified parallel random forest for intrusion detection systemsThe Journal of Supercomputing10.1007/s11227-016-1727-672:6(2235-2258)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s11227-016-1727-6
Kirubavathi Venkatesh GAnitha Nadarajan R(2012)HTTP botnet detection using adaptive learning rate multilayer feed-forward neural networkProceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems10.1007/978-3-642-30955-7_5(38-48)Online publication date: 20-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-30955-7_5
Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019

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Network Intrusion Detection System Using Neural Networks

System for Intrusion Detection with Artificial Neural Network

Intrusion Detection Based on RBF Neural Network

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