On atypical database transactions: Identification of probable frauds using machine learning for user profiling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX
Editors Anon
PublisherIEEE
Pages107-113
Number of pages7
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX - Newport Beach, CA, USA
Duration: 4 Nov 19974 Nov 1997

Other

OtherProceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX
CityNewport Beach, CA, USA
Period4/11/974/11/97

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  • Cite this

    Kokkinaki, A. I. (1997). On atypical database transactions: Identification of probable frauds using machine learning for user profiling. In Anon (Ed.), Proceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX (pp. 107-113). IEEE.