Skip to main content

Research Repository

Advanced Search

Dempster-Shafer for Anomaly Detection

Chen, Qi; Aickelin, Uwe


Qi Chen

Uwe Aickelin


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.


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
Peer Reviewed Peer Reviewed
Public URL


You might also like

Downloadable Citations