Abstract
This paper proposes a framework for deriving users' profiles of typical behaviour and detecting atypical transactions which may constitute fraudulent events or simply a change in user's behaviour. The anomaly detection problem is presented and previous attempts to address it are discussed. The proposed approach proves that individual users profiles can be constructed and provides an algorithm that derives users' profiles and an algorithm to identify atypical transactions. Lower and upper bounds for the number of misclassifications are also provided. An evaluation of this approach is discussed and some issues for further research are outlined.
Original language | English |
---|---|
Title of host publication | Proceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX |
Editors | Anon |
Publisher | IEEE |
Pages | 107-113 |
Number of pages | 7 |
Publication status | Published - 1997 |
Event | Proceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX - Newport Beach, CA, USA Duration: 4 Nov 1997 → 4 Nov 1997 |
Other
Other | Proceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX |
---|---|
City | Newport Beach, CA, USA |
Period | 4/11/97 → 4/11/97 |