@inproceedings { , title = {Dempster-Shafer for Anomaly Detection}, 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.}, conference = {Proceedings of the International Conference on Data Mining (DMIN 2006)}, organization = {Las Vegas, USA}, publicationstatus = {Published}, url = {https://nottingham-repository.worktribe.com/output/1018736}, author = {Chen, Qi and Aickelin, Uwe} }