Abstract
Recent activities in attacks on automated teller machines have shown a sophistication that has grown to a degree, where it is not always technically possible to prevent the attack. This paper describes an approach for anomaly and attack detection for ATMs. The approach works on multiple levels. First, we use sensor fusion on the low-level hardware sensors to get robust information about the device state. Second, we use a new model-based and self-learning anomaly detection method on the diagnosis data of all ATM devices to robustly detect anomalies in the system that might indicate an attack on the machine.
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Notes
- 1.
German project title: “Intelligente vernetzte Systeme für automatisierte Geldkreisläufe”.
- 2.
Intelligent Technical Systems OstWestfalenLippe: http://www.its-owl.de.
- 3.
Due to legacy support, this information is usually unencrypted.
- 4.
- 5.
- 6.
A PDFA corresponds to a PDTTA without transaction time probability function \(\tau \) (cf. Definition 1). For further information about probabilistic (timed) automata, refer to [1].
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Acknowledgements
This work was supported by the Wincor Nixdorf International GmbH, and partly funded by the German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster “Intelligent Technical Systems OstWestfalenLippe” (it’s OWL).
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Priesterjahn, S., Anderka, M., Klerx, T., Mönks, U. (2015). Generalized ATM Fraud Detection. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_13
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