Continuous Session Authentication via Motion Trigger Detection
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Solution Overview
Problem
Traditional security systems authenticate users only once and require re-authentication after a session has been inactive for a certain period, leading to inefficiencies and resource-intensive processes for continuous session management.
Innovation Solution
A continuous session authentication system that initiates an authentication state, switches to a monitoring state, detects re-authentication triggers based on motion data, and re-authenticates the session when necessary, using video frame analysis and biometric data to manage user sessions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems authenticate users only once and require re-authentication after inactivity, then the authentication process is simple and resource-efficient, but security is compromised during active sessions and user experience is degraded
Solution Approach 1:
The system dynamically transitions between authentication state and monitoring state based on session activity. During active sessions, the system monitors for re-authentication trigger events rather than requiring continuous re-authentication, adapting the security mechanism to the actual usage pattern and reducing unnecessary computational overhead while maintaining security.
Solution Approach 2:
The system automatically detects re-authentication trigger events through motion data analysis during monitoring state, eliminating the need for manual user intervention. The motion detection mechanism autonomously identifies when re-authentication is necessary based on detected motion patterns, reducing both user burden and system complexity.
2Reliability
If continuous authentication is performed using traditional methods, then security is maintained throughout the session, but computational resources and processing time are significantly increased
Solution Approach 1:
Instead of performing full authentication continuously, the system performs partial authentication by monitoring for re-authentication trigger events during monitoring state. This partial action approach maintains security by detecting significant changes in motion patterns while consuming far fewer computational resources than continuous full authentication would require.
Solution Approach 2:
The system periodically switches between authentication state and monitoring state rather than continuously performing authentication. This periodic action allows the system to maintain security at critical moments while reducing overall computational consumption during normal active sessions.
3Measurement precision
If motion data is continuously collected and analyzed during monitoring state, then re-authentication trigger events are detected accurately, but data processing load and system complexity increase
Solution Approach 1:
The system extracts and analyzes only the motion data relevant to detecting re-authentication trigger events during monitoring state, rather than processing all video data continuously. By focusing analysis on motion patterns and extracting only the necessary information, the system achieves accurate trigger detection while minimizing data processing load.
Solution Approach 2:
The system applies different processing quality to different aspects of the data. During monitoring state, the system performs lighter processing on motion data to detect trigger events, while performing full authentication processing only when necessary. This localized quality approach optimizes the balance between detection accuracy and processing efficiency.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for providing continuous session authentication and monitoring. An example method includes authenticating, at a first time, a session for a user of the client device based on an authentication image data structure and a plurality of first video frames captured before the first time. The example method further includes extracting sample data from a monitor region for each of a plurality of second video frames captured after the first time and generating motion data based on the extracted sample data. The example method further includes detecting, at a second time, a re-authentication trigger event based on the motion data. Subsequently, the example method includes re-authenticating the session based on the authentication image data structure and a plurality of third video frames captured after the second time.