[1O1] [General Session] 10. Vision / Speech

[1O1-05] ATM Anomaly Behavior Detection with Machine Learning: A Study

〇TRONG HUY PHAN¹, Reiko Kishi¹, Kazuma Yamamoto¹, Makoto Masuda¹ (1. Corporate Research and Development Center, Oki Electric Industry Co., Ltd.)

Keywords:Anomaly Detection, Machine Learning, ATM

In recent years, a steady increase in ATM (Automatic Teller Machine)-related crimes has been reported overseas. One of which is ATM skimming; the act of installing skimming devices (a.k.a. skimmers) to ATM to illegally copy information from the magnetic stripes of cash cards, credit cards, etc. As skimmers grow smaller and more sophisticated, detecting such devices with conventional sensors is facing great difficulties. With the purpose of strengthening ATM security, we are developing image sensing technologies that detect anomaly behaviors including ATM skimming acts using video feeds capturing the ATM operational area. Machine learning is employed to represent normal behaviors; the degrees of separation from such representation can be used as an indicator for abnormality level. In this article, we discuss the application of well-known methods (Subspace Representation of [Nanri 2004] and Gaussian Mixture Model of [Yu 2006]) to modelling ATM normal behaviors in order to detect ATM anomaly behaviors. Additionally, we also brief several considerations to realize high anomaly detection accuracy in real practice.