Loukas, George ORCID: https://orcid.org/0000-0003-3559-5182 and Oke, Gulay (2007) Likelihood ratios and recurrent random neural networks in detection of denial of service attacks. In: 2007 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, 16-18 July 2007, San Diego, CA, USA.

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Abstract

In a world that is becoming increasingly dependent on In- ternet communication, Denial of Service (DoS) attacks have evolved into a major security threat which is easy to launch but difficult to defend against. In order for DoS countermea- sures to be effective, the attack must be detected early and accurately. In this paper we propose a DoS detection tech- nique based on observation of the incoming traffic and a com- bination of traditional likelihood estimation with a recurrent random neural network (r-RNN) structure. We select input features that describe essential information on the incoming traffic and evaluate the likelihood ratios for each input, to fuse them with a r-RNN. We evaluate the performance of our method in terms of false alarm and correct detection rates with experiments on a large networking testbed, for a variety of input traffic.

Item Type:	Conference or Conference Paper (Paper)
Uncontrolled Keywords:	denial of service, network security, intrusion detection
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Organisation
Last Modified:	26 Nov 2020 22:35
URI:	http://gala.gre.ac.uk/id/eprint/15024

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