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A denial of service detector based on maximum likelihood detection and the random neural network

A denial of service detector based on maximum likelihood detection and the random neural network

Oke, Gulay and Loukas, George (2007) A denial of service detector based on maximum likelihood detection and the random neural network. Computer Journal, 50 (6). pp. 717-727. ISSN 0010-4620 (Print), 1460-2067 (Online) (doi:https://doi.org/10.1093/comjnl/bxm066)

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Abstract

In spite of extensive research in defence against De- nial of Service (DoS), such attacks remain a predom- inant threat in today’s networks. Due to the sim- plicity of the concept and the availability of the rele- vant attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of any comprehensive protection scheme against DoS is the detection of its existence, ideally long before the de- structive traffic build-up. In this paper we propose a generic approach for DoS detection which uses multi- ple Bayesian classifiers and random neural networks (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood esti- mation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven different implementations of it and evaluate our experimental results obtained in a large networking testbed.

Item Type: Article
Uncontrolled Keywords: Denial of service, Cyber security, Network security, Intrusion detection
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 28 Apr 2016 12:03
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/15020

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