Mobile Transaction Checkpoint Detection Using Sensor Context
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Solution Overview
Problem
There is a need to enhance the security of mobile device usage, particularly in electronic payment transactions, to prevent unauthorized access and ensure secure transfer of user credentials.
Innovation Solution
A system and method using contextual data from sensors on a user device to identify a starting checkpoint in a transaction, determine expected behavior through a machine learning model, and enable or terminate the entry of user credentials based on whether the expected behavior occurs, thereby detecting suspicious or non-suspicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile terminals enable electronic payment transactions in a convenient and flexible manner, then ease of operation is improved, but security risks increase
Solution Approach 1:
The system performs preliminary actions by capturing contextual data from sensors before the actual transaction occurs. The machine learning model analyzes this data in advance to predict suspicious activities, enabling preventive security measures to be taken before unauthorized transactions can complete, thus maintaining convenience while preemptively addressing security risks
Solution Approach 2:
The patent introduces an intermediary monitoring and detection component that sits between the user device and the transaction processing system. This intermediary captures contextual data from sensors, analyzes it using machine learning, and provides security verification without interfering with the normal transaction flow, thus maintaining ease of operation while adding security layers
2Reliability
If security measures are enhanced to detect suspicious activities, then reliability is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by enabling the user device to autonomously perform security monitoring and detection functions. The device's own sensors capture contextual data, and the integrated machine learning model analyzes this data locally to detect suspicious activities, eliminating the need for external security hardware or complex additional components
Solution Approach 2:
The patent applies multi-functionality by using the user device's existing sensors for dual purposes: their original functions plus security monitoring. The same camera, accelerometer, and other sensors used for normal device operation are also utilized to capture contextual data for transaction security verification, avoiding additional hardware complexity
Data Source
AI summary
Systems and methods are disclosed for detecting a suspicious and/or a non-suspicious activity during an electronic transaction performed by a user device. One method comprises identifying, by a monitoring and detection component, a starting check point in the electronic transaction. The monitoring and detection component may then receive contextual data from one or more sensors of the user device. Based on the contextual data and a machine learning model, the monitoring and detection component may determine whether an expected behavior occurred. Entry of user credentials may be enabled in response to determining that the expected behavior occurred, whereas the electronic transaction may be terminated in response to determining that the expected behavior did not occur.