CUED Publications database

Fast online anomaly detection using scan statistics

Turner, R and Ghahramani, Z and Bottone, S (2010) Fast online anomaly detection using scan statistics. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. pp. 385-390.

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We present methods to do fast online anomaly detection using scan statistics. Scan statistics have long been used to detect statistically significant bursts of events. We extend the scan statistics framework to handle many practical issues that occur in application: dealing with an unknown background rate of events, allowing for slow natural changes in background frequency, the inverse problem of finding an unusual lack of events, and setting the test parameters to maximize power. We demonstrate its use on real and synthetic data sets with comparison to other methods. ©2010 IEEE.

Item Type: Article
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 08 Apr 2021 06:23
DOI: 10.1109/MLSP.2010.5589151