Monitoring and predicting physical force attacks on transaction terminals

A system, comprising:

• a processor;
• a memory coupled to the processor;
• and
• the processor configured to perform operations comprising:
• deriving factors associated with past brute force attacks on terminals within a given area over a given period of time, wherein the factors comprise a total number of the past brute force attacks, a severity of each past brute force attack, a time of day of each past brute force attack, a calendar date for each past brute force attack, a day of week for each past brute force attack, and weather experienced in the given area for each past brute force attack;
• extracting visual features from past video captured of the past brute force attacks, wherein the visual features are associated with individuals of the past brute force attacks, vehicles of the past brute force attacks, orientations of the vehicles relative to corresponding terminals of the past brute force attacks, and objects held by or in proximity to the individuals during the past brute force attacks;
• training a machine learning model (model) to predict in real time a future brute force attack when provided the factors and the visual features as input;
• providing current factors and current visual features as input for a given terminal located within the given area;
• and
• sending a real-time alert when a confidence value returned by the model as output indicates an in-progress brute force attack at the given terminal.

A method, comprising:

• obtaining factors associated with past physical attacks on terminals within a given area over a given period of time, wherein the factors comprise a total number of the past physical attacks, a severity of each past physical attack, a time of day of each past physical attack, a calendar date for each past physical attack, a day of week for each past physical attack, and weather experienced in the given area for each past physical attack;
• extracting visual features from video captured of the past physical attacks, wherein the visual features are associated with individuals of the past physical attacks, vehicles of the past physical attacks, orientations of the vehicles relative to corresponding terminals of the past physical attacks, and objects held by or in proximity to the individuals during the past physical attacks;
• training a machine learning model to predict a future physical attack when provided the factors and the visual features as input;
• extracting current visual features from current video for a given terminal located within the given area;
• and
• sending a real-time alert when a confidence value returned by the machine learning model indicates a potential physical attack at the given terminal.