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Unsupervised Anomaly Detection System Using Next-Generation Router Architecture

Published

Author(s)

Richard A. Rouil, Nicolas Chevrollier, Nada T. Golmie

Abstract

Unlike many intrusion detection systems that rely mostly on labeled training data, we propose a novel technique for anomaly detection based on unsupervised learning and we apply it to counter denial-of-service attacks. Initial simulation results suggest that significant improvements can be obtained. We then discuss an implementation of our anomaly detection system in the ForCES router architecture and evaluate it using attack traffic.
Proceedings Title
IEEE Military Communications Conference MILCOM 2005
Conference Dates
October 17-20, 2005
Conference Location
Atlantic City, NJ, USA

Citation

Rouil, R. , Chevrollier, N. and Golmie, N. (2005), Unsupervised Anomaly Detection System Using Next-Generation Router Architecture, IEEE Military Communications Conference MILCOM 2005, Atlantic City, NJ, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=150114 (Accessed June 23, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 30, 2005, Updated October 12, 2021