Mobile Authentication Using Machine-Learned Motion Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing user authentication methods on mobile devices, such as passcodes and biometrics, are vulnerable to circumvention and theft, posing a security risk as smartphones become gateways to sensitive systems and IoT devices.
Innovation Solution
Implement machine-learning based user authentication using motion and input patterns on mobile devices, employing sensors like accelerometers and pressure sensors to recognize authorized user habits and patterns, and alternate authentication features like video and audio comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional passcodes and biometrics are used for authentication, then ease of operation is maintained, but security reliability deteriorates due to vulnerability to circumvention and theft
Solution Approach 1:
The patent transforms authentication from static parameters (passcodes, fixed biometrics) to dynamic parameters by incorporating real-time motion data, pressure sensor readings, and usage pattern analysis. The authentication model continuously learns and adapts to user behavior patterns, changing the authentication parameters dynamically to prevent circumvention while maintaining ease of use for authorized users.
Solution Approach 2:
The authentication system transitions from static verification to dynamic verification by continuously monitoring motion patterns, device handling behaviors, and usage contexts. The machine learning model adapts to changing user behaviors over time, making the authentication system dynamic and resilient to stolen credentials while maintaining smooth operation for legitimate users.
2Reliability
If machine-learning based authentication with multiple sensors is implemented, then authentication reliability improves, but device complexity increases
Solution Approach 1:
The patent leverages existing multi-functional mobile device components (accelerometers, pressure sensors, cameras, microphones) already present for other purposes, making them serve dual functions including authentication. This approach increases reliability through multiple sensing modalities without proportionally increasing device complexity, as these components are already integrated into modern smartphones.
Solution Approach 2:
The machine learning authentication model operates autonomously, continuously learning from user interactions and automatically updating authentication parameters without requiring manual configuration or intervention. The system self-calibrates and adapts to new users and behaviors, reducing the operational complexity despite the sophisticated underlying algorithms.
Data Source
AI summary
Machine-learning based user authentication using a mobile device (e.g., using a computerized tool) is enabled. For example, a non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining an input received via a mobile device, determining, based on the input and using an authentication model, whether the input threshold matches an input pattern associated with an authorized user profile authorized to access a feature of the mobile device, wherein the input pattern has been determined based on machine learning applied to past inputs at the mobile device other than the input, and wherein the authentication model has been generated based on the machine learning applied to the input pattern, and based on a determination that the input at the mobile device is associated with an authorized user profile, granting access to the feature of the mobile device.