Autonomic diagnosis of anomalous network traffic

Marnerides, A., Pezaros, D. and Hutchison, D. (2010) Autonomic diagnosis of anomalous network traffic. In: Third IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications, IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2010), Montreal, Canada, 14-17 June 2010, (doi:10.1109/WOWMOM.2010.5534933)

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Abstract

Network traffic abnormalities pose one of the greatest threats for networked environments. Autonomic communications offer a solution: it should be possible to design network mechanisms that behave adaptively and respond to any anomalous phenomenon that threatens normal network behaviour. In this paper we present the design of an adaptive anomaly detection component that has been built as part of an autonomic network system. We have implemented an entropy estimator to predict the onset of anomalous traffic behaviour within an autonomic resilience framework, and a Supervised Naïve Bayesian classifier which synergistically empower the core properties of self-adaptation, self-learning and self-protection for next generation networks. Being part of an always-on, automated measurement and control infrastructure, such mechanism enforces the adaptive system reaction to suboptimal network operation and its subsequent restoration, while requiring minimal static (re)configuration and operator intervention.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pezaros, Dr Dimitrios
Authors: Marnerides, A., Pezaros, D., and Hutchison, D.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Embedded, Networked, and Distributed Systems (ENDS)

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