Dempster-Shafer for Anomaly Detection
Chen, Qi; Aickelin, Uwe
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|
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