Dempster-Shafer for Anomaly Detection
Chen, Qi and Aickelin, Uwe (2006) Dempster-Shafer for Anomaly Detection. In: Proceedings of the International Conference on Data Mining (DMIN 2006), Las Vegas, USA.
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.
Actions (Archive Staff Only)