Monitoring and predicting physical force attacks on transaction terminals

Abstract

Factors associated with past brute force attacks on terminals within a given area are derived. The factors include a total number of the past brute force attacks, a severity, a time of day, a calendar date, a day of week, and weather experienced in the given area for each past brute force attack. Visual features are extracted from past video captured of the past brute force attacks. The visual features are associated with individuals, vehicles, orientations of vehicles relative to corresponding terminals, and objects held by or in proximity to the individuals. A machine learning model is trained to predict in real time a future brute force attack when provided the factors and the visual features as input. Current factors and visual features are provided as input for a given terminal, and a real-time alert is sent when a confidence value returned by the model indicates an in-progress attack.