Behavioral Biometric Authentication for Secure Password Reset
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Solution Overview
Problem
Existing password reset processes are vulnerable to malicious attacks and user inconvenience due to reliance on insecure external communications and outdated recovery information, making it difficult to balance security and usability.
Innovation Solution
A system that uses machine learning models to authenticate password reset requests based on historical behavioral biometric metadata, such as keystroke dynamics and touchscreen interactions, generating user profiles and performing biometric matches to ensure secure and transparent authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional password reset processes use email or security questions for authentication, then users can recover their accounts, but the system becomes vulnerable to malicious attacks and security breaches
Solution Approach 1:
The patent extracts the authentication process from external vulnerable systems (email servers, security question databases) and relocates it to the client device where behavioral biometric data is naturally captured. This removes the attack surface associated with external communication channels while maintaining authentication functionality through on-device machine learning models that analyze typing patterns and interaction behaviors.
Solution Approach 2:
The patent introduces behavioral biometric data as an intermediary between the user and the authentication system. Instead of directly using vulnerable external systems, the system captures subtle behavioral characteristics (typing rhythm, keystroke dynamics, touchscreen interaction patterns) that serve as a secure mediator for verifying user identity without exposing the system to external attacks.
2Reliability
If password reset processes implement strong security measures, then account security is improved, but user convenience and ease of operation deteriorate
Solution Approach 1:
The patent implements self-service authentication where the system automatically captures and analyzes behavioral biometric data without requiring user intervention. The machine learning model runs locally on the user's device, continuously learning their typing patterns and interaction behaviors, and automatically verifies identity during password reset requests without burdening the user with additional security steps.
Solution Approach 2:
The patent changes the authentication parameter from traditional knowledge-based factors (passwords, security questions) to behavioral biometric factors (typing rhythm, keystroke dynamics). This parameter change maintains high security while improving user convenience because the behavioral verification happens transparently in the background during normal interaction, requiring no additional user effort.
3Adaptability or versatility
If the system relies on external communication systems for password reset, then account recovery is enabled, but the system becomes susceptible to interception and compromise
Solution Approach 1:
The patent extracts the password reset authentication process from external communication channels (email, SMS) and relocates it entirely to the client device. By using on-device behavioral biometric verification, the system eliminates the need for external communication systems in the authentication flow, thereby removing the vulnerability to interception while preserving account recovery functionality.
Solution Approach 2:
The patent substitutes the mechanical system of external communication (email delivery, SMS transmission) with a computational system based on behavioral biometric analysis. Instead of sending reset links through vulnerable communication channels, the system uses machine learning models to verify user identity through behavioral patterns captured during normal device interaction, replacing the communication-based mechanism with a computation-based verification.
Data Source
AI summary
A system includes memory hardware configured to store instructions and one or more electronic processors configured to execute the instructions. The instructions include logging historical behavioral biometric metadata from one or more computing platforms, generating a user profile based on the logged historical behavioral biometric metadata, receiving an authentication request from a client computing platform, providing the first behavioral biometric metadata and the user profile to a trained machine learning model to generate a biometric match, generating a positive control signal in response to a positive biometric match, sending the positive control signal to the client computing platform, updating the historical behavioral biometric metadata with the first behavioral biometric metadata, and retraining the trained machine learning model using the updated historical behavioral biometric metadata. The authentication request includes first behavioral biometric metadata.