Identity Verification Risk Scoring for Continuous Access Control
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Solution Overview
Problem
Existing security systems fail to validate a user's identity accurately and are prone to compromise, often requiring additional hardware and being inflexible in adapting to varying access conditions and user roles or contexts.
Innovation Solution
A user identification system that uses machine-learning techniques to analyze motion and non-motion data from sensors to generate unique user signatures, providing continuous authentication and adaptive access control based on operational contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication methods (passwords, access cards) are used, then implementation is simple and widespread, but they fail to validate user identity accurately and are prone to compromise
Solution Approach 1:
The patent replaces mechanical authentication systems (passwords, access cards) with a sensor-based motion analysis system. Motion sensors capture characteristic movement patterns, and machine learning algorithms analyze these patterns to validate identity, substituting physical authentication artifacts with behavioral biometrics that are harder to compromise.
Solution Approach 2:
The system creates digital copies of user motion patterns through sensor data. Instead of relying on physical credentials, the system captures and analyzes replicated movement characteristics via accelerometers and gyroscopes, generating a digital behavioral profile that serves as the authentication basis.
2Reliability
If multi-factor authentication techniques are implemented, then the difficulty to impersonate a user increases, but additional hardware requirements and implementation challenges arise
Solution Approach 1:
The patent makes the mobile device itself multi-functional by using its existing sensors (accelerometer, gyroscope, camera) for both standard operations and security authentication. The same device users already carry and interact with becomes the authentication instrument, eliminating the need for separate security hardware while maintaining impersonation resistance.
Solution Approach 2:
The system uses the user's own motion patterns and device handling behavior as the authentication mechanism. The device leverages the user's natural interaction patterns with it, turning the user's own behavior into the security credential without requiring external authentication systems or additional hardware components.
3Productivity
If access is granted based on password or security card receipt, then access control decision is made quickly, but access is granted for longer period than appropriate
Solution Approach 1:
The patent implements continuous authentication by continuously monitoring motion sensor data during the user session. Instead of a one-time authentication check, the system continuously analyzes movement patterns to verify the user's identity throughout the access period, maintaining security while enabling uninterrupted legitimate access.
Solution Approach 2:
The system provides continuous feedback by monitoring motion patterns in real-time and adjusting access status based on ongoing verification. If anomalous motion patterns are detected or the user stops interacting with the device, the system can revoke access dynamically, ensuring access duration remains appropriate to actual user presence and behavior.
4Adaptability or versatility
If conventional security systems are used, then implementation is straightforward, but they cannot adapt to varying access conditions and user roles
Solution Approach 1:
The patent implements dynamic authentication by adjusting security parameters based on real-time context. The system evaluates motion patterns, device usage context, location, and time to dynamically determine authentication requirements and access decisions, allowing the same system to adapt to different user roles, security zones, and situational contexts without manual reconfiguration.
Data Source
AI summary
A system is disclosed for identifying a user based on the classification of user characteristic data. An identity verification system receives a request from a requesting target user for access to an operational context and characteristic data describing actions of the requesting target user. The identity verification system inputs the characteristic data to an identity confidence model to determine an identity confidence value describes a likelihood that an identity of the requesting target user matches an authenticating identity and determines a false match rate and false non-match rate, which represent a performance of the identity confidence model. The identity verification system determines a match probability for the requesting target user by adjusting the identity confidence value based on the determined false match rate and false non-match rate and grants the requesting target user access to the operational context if match probability is greater than the operational security threshold.