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

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    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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