Iordache, M., Jouet, S., Marnerides, A. K. and Pezaros, D. P. (2017) Distributed, Multi-Level Network Anomaly Detection for Datacentre Networks. In: IEEE ICC 2017 Next Generation Networking and Internet Symposium, Paris, France, 21-25 May 2017, ISBN 9781467389990 (doi: 10.1109/ICC.2017.7996569)
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Abstract
Over the past decade, numerous systems have been proposed to detect and subsequently prevent or mitigate security vulnerabilities. However, many existing intrusion or anomaly detection solutions are limited to a subset of the traffic due to scalability issues, hence failing to operate at line-rate on large, highspeed datacentre networks. In this paper, we present a two-level solution for anomaly detection leveraging independent execution and message passing semantics. We employ these constructs within a network-wide distributed anomaly detection framework that allows for greater detection accuracy and bandwidth cost saving through attack path reconstruction.Experimental results using real operational traffic traces and known network attacks generated through the Pytbull IDS evaluation framework, show that our approach is capable of detecting anomalies in a timely manner while allowing reconstruction of the attack path, hence further enabling the composition of advanced mitigation strategies. The resulting system shows high detection accuracy when compared to similar techniques, at least 20% better at detecting anomalies, and enables full path reconstruction even at smallto- moderate attack traffic intensities (as a fraction of the total traffic), saving up to 75% of bandwidth due to early attack detection.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Jouet, Mr Simon and Iordache Sica, Mr Mircea and Pezaros, Professor Dimitrios |
| Authors: | Iordache, M., Jouet, S., Marnerides, A. K., and Pezaros, D. P. |
| College/School: | College of Science and Engineering > School of Computing Science |
| ISSN: | 1938-1883 |
| ISBN: | 9781467389990 |
| Copyright Holders: | Copyright © 2017 IEEE |
| First Published: | First published in 2017 IEEE International Conference on Communications (ICC) |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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