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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
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 October 7, 2025)