Monitoring and predicting physical force attacks on transaction terminals
A server obtains real-time video depicting an area that includes a terminal. The server analyzes the video for depictions of a vehicle and objects associated with attacks on terminals, A server provides factors associated with the analysis to a machine learning model (MLM). The server receives a confidence value as output from the MLM. The server sends an alert when the confidence value exceeds a threshold as an indication that a potential attack on the terminal may be in progress.
1. A method, comprising:
obtaining real-time video depicting an area having a terminal;
analyzing the real-time video for depictions of a vehicle and objects associated with the use of brute force;
providing factors associated with the analyzing to a machine learning model (MLM) and receiving a confidence value as output from the MLM, wherein the factors include features derived from the real-time video associated with the vehicle, the objects, and an individual associated with the vehicle and the objects, and wherein the factors further include a total number of past brute force attacks in the area and a severity level associated with each past brute force attack; and
sending an alert when the confidence value exceeds a threshold.
2. The method of claim 1 , wherein analyzing further includes determining a size, a color, a make, and a vehicle model of the vehicle from the real-time video.
3. The method of claim 2 , wherein analyzing further includes determining a height, hair color, complexion, clothing, jewelry, facial features, and any tattoos of the individual from the real-time video.
4. The method of claim 1 , wherein analyzing further includes identifying the individual at the terminal from the real-time video.
5. The method of claim 4 , wherein analyzing further includes determining whether any of the objects are being held or operated by the individual from the real-time video.
6. The method of claim 1 , wherein analyzing further includes generating the factors based on the analyzing for the vehicle, the objects, and the at least one individual depicted in the real-time video.
7. The method of claim 6 , wherein generating further includes adding a current time of day, a current day of week, a current calendar date, and current weather at a given location associated with the terminal to the factors.
8. The method of claim 1 , wherein receiving the confidence value further includes modifying the confidence value based on obtained information for one or more of the vehicle or the individual depicted in the real-time video, wherein the obtained information is obtained from one or more external systems.
9. The method of claim 1 , wherein sending further includes causing activation of dye packs within media cassettes of the terminal to render currency unusable in a safe of the terminal when the alert is provided.
10. The method of claim 1 , wherein sending further includes causing the terminal to shut down for operation when the alert is provided.
11. The method of claim 1 , wherein sending further includes activating a microphone associated with the terminal and a camera not associated with the real-time video to capture audio features and visual features of the individual depicted in the real-time video, wherein the audio features include speech of the individual and the speech is provided with the alert.
12. The method of claim 1 , wherein sending further includes assembling a packet of information when the alert is provided, wherein the packet information includes a video clip taken from the real-time video and motor vehicle information for the vehicle obtained through a vehicle registration system and providing the packet of information with the alert.