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

Georgios Loukas*, Gülay Öke

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Several of todays' computing challenges have been met by resorting to and adapting optimal solutions that have evolved in nature. For example, autonomic communication networks 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.

Original languageEnglish
Title of host publication2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS - Pisa, Italy
Duration: 8 Oct 200711 Oct 2007

Publication series

Name2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS

Conference

Conference2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, MASS
Country/TerritoryItaly
CityPisa
Period8/10/0711/10/07

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