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A biologically inspired denial of service detector using the random neural network

A biologically inspired denial of service detector using the random neural network

Loukas, George and Oke, Gulay (2007) A biologically inspired denial of service detector using the random neural network. In: 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems. IEEE, Piscataway, NJ, US. ISBN 9781424414543 ISSN 2155-6806

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Abstract

Several of today’s computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication net- works have started applying biologically-inspired methods to achieve some of their self-* properties. We build upon such methods to solve the recent problem of detection of Denial of Service networking attacks, by proposing a combination of Bayesian decision making and the Random Neural Networks (RNN) which are inspired by the random spiking behaviour of the biological neurons. Our approach is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood estimation and fusing the information gathered from the individual input features using different architectures of the RNN. The experiments are conducted using the CPN networking protocol which is also based on the RNN.

Item Type: Conference Proceedings
Title of Proceedings: 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems
Additional Information: IEEE International Conference on Mobile Ad-hoc and Sensor Systems, 8-11 October 2007, Pisa, Italy
Uncontrolled Keywords: denial of service, network security, intrusion detection, cyber security
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 03 Jun 2016 14:37
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/15021

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