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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
Proceedings of 33rd Annual International Symposium on Computer Architecture
Conference Location
, USA

Citation

Rouil, R. , Chevrollier, N. and Golmie, N. (2006), Unsupervised Anomaly Detection System Using Next-Generation Router Architecture, Proceedings of 33rd Annual International Symposium on Computer Architecture, , USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=150115 (Accessed April 21, 2024)
Created September 30, 2006, Updated October 12, 2021