Dempster-Shafer for Anomaly Detection

Chen, Qi; Aickelin, Uwe

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. Presented at Proceedings of the International Conference on Data Mining (DMIN 2006)

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