Behavior-Based Mobile Authentication Using Gross Motor Patterns
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Solution Overview
Problem
Existing authentication methods for mobile, portable communication systems, such as PINs and passwords, are cumbersome and insecure, while fingerprint sensors are unreliable due to environmental factors and user-specific conditions, necessitating an improved authentication method that is user-friendly and secure.
Innovation Solution
A behavior-based authentication system utilizing sensors to detect gross motor movements, application usage patterns, and biometric data, without requiring external network connections, to authenticate users based on their unique behavior patterns, leveraging machine learning to recognize and verify user identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PIN or password authentication is used, then security can be provided, but user convenience deteriorates due to the need to remember multiple credentials
Solution Approach 1:
The system performs automatic authentication by analyzing user behavior patterns without requiring the user to manually enter credentials. The processor continuously monitors interaction parameters and automatically compares them against stored profiles to authenticate the user, eliminating the need for manual PIN or password entry while maintaining security through behavioral analysis
2Ease of operation
If fingerprint sensor is used, then authentication can be performed without remembering credentials, but reliability deteriorates due to environmental factors and user conditions
Solution Approach 1:
The system transitions from relying on a single biometric parameter (fingerprint) to analyzing multiple behavioral parameters simultaneously. The processor monitors various interaction characteristics including typing patterns, swipe gestures, and device handling behaviors, creating a composite authentication profile that remains reliable under diverse environmental conditions where fingerprint sensors may fail
3Reliability
If complex passwords are used, then security is improved, but ease of operation deteriorates due to difficulty in remembering long and complex credentials
Solution Approach 1:
The system replaces the mechanical approach of manual credential entry with an automated sensor-based detection system. Instead of requiring users to type complex passwords, the system uses processors and sensors to automatically capture and analyze behavioral patterns during natural device interaction, providing high security through automated behavioral biometrics rather than manual credential input
Data Source
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AI summary
The invention relates to a method for behavior-based authentication (400) of a current user (1) of a mobile, portable communication system (100), which has at least one sensor (110) for detecting gross motor measurement data (500), a gross motor classification module (200), a processor (130), and an internal memory (120). Furthermore, a user is registered in the mobile, portable communication system (100). The sensor (110) is designed to detect the gross motor measurement data (500) of a gross motor movement of the current user (1) of the mobile, portable communication system (100) and the gross motor classification module (200) is trained to detect a generic gross motor movement pattern by means of training data sets of a user cohort. In addition, the gross motor classification module (200) implements a machine learning method. The gross motor classification module (200) is executed by the processor (130) of the mobile, portable communication system (100).