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.

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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.

Item Type:Conference or Workshop Item (Paper)
Schools/Departments:Faculty of Science > School of Computer Science and Information Technology
ID Code:596
Deposited By:Aickelin, Professor Uwe
Deposited On:17 Oct 2007 14:07
Last Modified:17 Oct 2007 14:07

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