Dempster-Shafer for Anomaly Detection

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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)
RIS ID: https://nottingham-repository.worktribe.com/output/1018736
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 17 Oct 2007 13:07
Last Modified: 04 May 2020 20:29
URI: https://eprints.nottingham.ac.uk/id/eprint/596

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