Oke, Gulay, Loukas, George ORCID: 0000-0003-3559-5182 and Gelenbe, Erol (2007) Detecting denial of service attacks with Bayesian classifiers and the random neural network. In: 2007 IEEE International Conference on Fuzzy Systems. IEEE, London, UK. ISBN 9781424412099 ISSN 1098-7584 (doi:https://doi.org/10.1109/FUZZY.2007.4295666)

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Abstract

Denial of Service (DoS) is a prevalent threat in today’s networks. While such an attack is not difficult to launch, defending a network resource against it is disproportionately difficult, and despite the extensive research in recent years, DoS attacks continue to harm. The first goal of any protection scheme against DoS is the detection of its existence, ideally long before the destructive traffic build-up. In this paper we propose a generic approach which uses multiple Bayesian classifiers, and we present and compare four different implementations of it, combining likelihood estimation and the Random Neural Network (RNN). The RNNs are biologically inspired structures which represent the true functioning of a biophysical neural network, where the signals travel as spikes rather than analog signals. We use such an RNN structure to fuse real-time networking statistical data and distinguish between normal and attack traffic during a DoS attack. We present experimental results obtained for different traffic data in a large networking testbed.

Item Type:	Conference Proceedings
Title of Proceedings:	2007 IEEE International Conference on Fuzzy Systems
Additional Information:	© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. IEEE International Fuzzy Systems Conference, 23-26 July 2007, London UK.
Uncontrolled Keywords:	intrusion detection, denial of service, network security
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	26 Nov 2020 22:35
URI:	http://gala.gre.ac.uk/id/eprint/15022

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