Skip navigation

Detecting denial of service attacks with Bayesian classifiers and the random neural network

Detecting denial of service attacks with Bayesian classifiers and the random neural network

Oke, Gulay, Loukas, George 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)

[img]
Preview
PDF (Author's Accepted Manuscript)
15022_Loukas_Detecting denial of service attacks (AAM) 2007.pdf - Accepted Version

Download (815kB) | Preview

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 / Department / Research Group: Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 03 Jun 2016 11:05
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/15022

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics