Remediating anomalous traffic behaviour in future networked environments

Marnerides, A. K. , Jakeman, M., Hutchison, D. and Pezaros, D. P. (2011) Remediating anomalous traffic behaviour in future networked environments. In: Szabó, R., Zhu, H., Imre, S. and Chaparadza, R. (eds.) Access Networks: 5th International ICST Conference on Access Networks, AccessNets 2010 and First ICST International Workshop on Autonomic Networking and Self-Management in Access Networks, SELFMAGICNETS 2010, Budapest, Hungary, November 3-5, 2010, Revised. Series: Lecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering (63). Springer: Heidelberg ; New York, pp. 187-197. ISBN 9783642209307 (doi: 10.1007/978-3-642-20931-4_15)

Abstract

The diverse characteristics of network anomalies, and the specific recovery approaches that can subsequently be employed to remediate their effects, have generally led to defence mechanisms tuned to respond to specific abnormalities; and they are often suboptimal for providing an overall resilience framework. Emerging future network environments are likely to require always-on, adaptive, and generic mechanisms that can integrate with the core networking infrastructure and provide for a range of self-* capabilities, ranging from self-protection to self-tuning. In this paper we present the design and implementation of an adaptive remediation component built on top of an autonomic network node architecture. A set of pluggable modules that employ diverse algorithms, together with explicit cross-layer interaction, has been engineered to mitigate different classes of anomalous traffic behaviour in response to both legitimate and malicious external stimuli. In collaboration with an always-on measurement-based anomaly detection component, our prototype facilitates the properties of self-optimisation and self-healing.

• Item Type: Book Sections
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Marnerides, Dr Angelos and Pezaros, Professor Dimitrios
• Authors: Marnerides, A. K., Jakeman, M., Hutchison, D., and Pezaros, D. P.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783642209307