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Authors: Maik Anderka 1 ; Timo Klerx 1 ; Steffen Priesterjahn 2 and Hans Kleine Büning 1

Affiliations: 1 University of Paderborn, Germany ; 2 Wincor Nixdorf International GmbH, Germany

Keyword(s): ATM Fraud Detection, Sequence-based Anomaly Detection, Automatic Model Generation.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Knowledge Acquisition and Representation ; Learning in Process Automation ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: Because of the direct access to cash and customer data, automated teller machines (ATMs) are the target of manifold attacks and fraud. To counter this problem, modern ATMs utilize specialized hardware security systems that are designed to detect particular types of attacks and manipulation. However, such systems do not provide any protection against future attacks that are unknown at design time. In this paper, we propose an approach that is able to detect known as well as unknown attacks on ATMs and that does not require additional security hardware. The idea is to utilize automatic model generation techniques to learn patterns of normal behavior from the status information of standard devices comprised in an ATM; a significant deviation from the learned behavior is an indicator of a fraud attempt. We cast the identification of ATM fraud as a sequence-based anomaly detection problem, and we describe three specific methods that implement our approach. An empirical evaluation using a real-world data set that has been recorded on a public ATM within a time period of nine weeks shows promising results and underlines the practical applicability of the proposed approach. (More)

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Paper citation in several formats:
Anderka, M.; Klerx, T.; Priesterjahn, S. and Kleine Büning, H. (2014). Automatic ATM Fraud Detection as a Sequence-based Anomaly Detection Problem. In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-018-5; ISSN 2184-4313, SciTePress, pages 759-764. DOI: 10.5220/0004922307590764

@conference{icpram14,
author={Maik Anderka. and Timo Klerx. and Steffen Priesterjahn. and Hans {Kleine Büning}.},
title={Automatic ATM Fraud Detection as a Sequence-based Anomaly Detection Problem},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2014},
pages={759-764},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004922307590764},
isbn={978-989-758-018-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Automatic ATM Fraud Detection as a Sequence-based Anomaly Detection Problem
SN - 978-989-758-018-5
IS - 2184-4313
AU - Anderka, M.
AU - Klerx, T.
AU - Priesterjahn, S.
AU - Kleine Büning, H.
PY - 2014
SP - 759
EP - 764
DO - 10.5220/0004922307590764
PB - SciTePress

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