Presentation title

ATM Protection Using Embedded Machine Learning Solutions

Authors

Antonio Rizzo, Alessandro Rossi, Francesco Montefoschi, Carlo Festucci and Maurizio Caporali

Institution(s)

Università di Siena, Italy

Presentation type

Technical presentation

Abstract

ATMs are an easy target for fraud attacks, like card skimming/trapping, cash trapping, malware and physical attacks. Attacks based on explosives are a rising problem in Europe and many other parts of the world. A report from the EAST association shows a rise of 80% of such attacks between the first six months of 2015 and 2016. This trend is particularly worrying, not only for the stolen cash, but also for the significant collateral damages to buildings and equipment. We developed an embedded video surveillance application based on Intel RealSense depth cameras that can run on Seco’s A80 Single Board Computer. The camera is embedded in the ATM’s chassis, and focus the area under the screen, where explosive based attacks begin. The use of depth cameras avoids privacy-related regulatory issues. The computer vision analysis rests on Machine Learning algorithms. We designed a model based on Convolutional Neural Networks able to discriminate between regular ATM usage and breaking attempts. The dataset has been built by recording and tagging depth videos where different people stage withdrawals and attacks on a retired ATM, replicating the actions the thieves do, thanks to the knowledge of the Security Department of the Monte dei Paschi di Siena Bank. The results show that the implemented architecture is able to classify depth data in real-time on an embedded system, detecting all the test attacks in a few seconds.

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Additional material

• Presentation slides: [pdf]