Monolithic sensor device, protection system for detecting tampering in ATM terminals and method for recognizing tampering patterns in ATM terminals

The system uses AI-based pattern recognition with audio, vibration, and infrared sensors to distinguish ATM tampering from environmental disturbances, enhancing security and reducing power consumption.

WO2025208194A1 Publication Date: 2025-10-09PROCOMP AMAZONIA IND ELECTRONICSA
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Patent Information

Application Number
PCT/BR2025/050119
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-04-05
Filing Date
2025-03-28
Publication Date
2025-10-09

AI Technical Summary

Technical Problem

Existing ATM security systems face challenges in distinguishing between real attacks and environmental disturbances, particularly with seismic sensors that generate false alerts from external factors like heavy trucks or subway trains, and mesh networks are costly and have coverage gaps.

Method used

A system utilizing a processing platform with artificial intelligence for pattern recognition, combining audio, vibration, and infrared sensors, employing deep learning to differentiate between tampering attempts and normal environmental disturbances, with a fraud detection system and energy-efficient design.

Benefits of technology

Effectively identifies tampering attempts on ATMs by differentiating sound recordings and vibrations from normal environmental conditions, reducing false alarms and maintaining low power consumption while ensuring robust security.

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Abstract

The present invention provides a system, method, and intelligent device for detecting attempts to tamper with automated terminals. The system comprises a processing unit, a data acquisition subsystem, a self-test subsystem, a power monitoring system, a tamper detection system and an intelligent agent system. The system is in communication with a sensor system comprising a plurality of sensors which provide monitoring information of the ATM terminal, such as vibration, brightness and audio aspects. By means of an artificial intelligence model using a convolutional neural network (CNN), the system can recognise patterns and distinguish a real attack from normal disturbances in the environment.
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