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Dempster-Shafer for Anomaly Detection

Chen, Qi; Aickelin, Uwe

Authors

Qi Chen

Uwe Aickelin



Abstract

In this paper, we implement an anomaly detection system using the Dempster-Shafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach to a real-world email dataset the algorithm works for email worm detection. Dempster-Shafer can be a promising method for anomaly detection problems with multiple features (data sources), and two or more classes.

Citation

Chen, Q., & Aickelin, U. Dempster-Shafer for Anomaly Detection.

Conference Name Proceedings of the International Conference on Data Mining (DMIN 2006)
Deposit Date Oct 17, 2007
Publicly Available Date Mar 29, 2024
Peer Reviewed Peer Reviewed
Public URL https://nottingham-repository.worktribe.com/output/1018736

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