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

Gülay Öke*, George Loukas, Erol Gelenbe

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

56 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Fuzzy Systems, FUZZY
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom
Duration: 23 Jul 200726 Jul 2007

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2007 IEEE International Conference on Fuzzy Systems, FUZZY
Country/TerritoryUnited Kingdom
CityLondon
Period23/07/0726/07/07

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