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Likelihood ratios and recurrent random neural networks in detection of denial of service attacks

Likelihood ratios and recurrent random neural networks in detection of denial of service attacks

Loukas, George 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 / Department / Research Group: Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Related URLs:
Last Modified: 25 May 2016 09:33
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/15024

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