Behavioral Identity Fingerprinting for Continuous Authentication
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Solution Overview
Problem
Current authentication methods rely heavily on credentials like passwords and physical tokens, which are vulnerable to theft and hacking, and often only verify identity at login, failing to provide continuous security and preventing identity theft.
Innovation Solution
The implementation of multi-factor identity fingerprinting, which generates a unique identity fingerprint based on user behavior patterns, preferences, and usage history, allowing for continuous authentication without interrupting user sessions and providing enhanced security by correlating user actions with stored historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional credential-based authentication (passwords, tokens) is used, then identity verification can be performed, but security is vulnerable to theft, hacking, and credential compromise
Solution Approach 1:
The patent replaces traditional mechanical credential verification (passwords, tokens) with a behavioral biometric system that analyzes user interaction patterns. Instead of relying on memorized secrets or physical tokens that can be stolen, the system captures and analyzes behavioral data such as typing patterns, mouse movements, and navigation behaviors to create a dynamic identity fingerprint that is difficult to replicate or steal.
Solution Approach 2:
The system creates a behavioral copy or fingerprint of the user's interaction patterns rather than relying on static credentials. This behavioral profile captures the unique way a user interacts with the system, providing a replicable yet secure authentication mechanism that reflects the user's natural behavior rather than their memorized credentials.
2Reliability
If continuous authentication is implemented to improve security, then identity theft can be prevented, but user experience is interrupted by frequent verification requests
Solution Approach 1:
The system performs continuous authentication in the background without interrupting the user's workflow. Behavioral data is collected and analyzed continuously during normal system usage, allowing the system to maintain security verification while the user interacts with the system naturally, eliminating the need for periodic pause-and-verify interruptions.
Solution Approach 2:
The authentication system operates autonomously by continuously monitoring user behavior and automatically verifying identity without requiring user intervention. The system self-manages the authentication process by comparing real-time behavioral patterns against the stored identity fingerprint, providing continuous security verification without user awareness or action.
3Measurement precision
If behavioral data collection is implemented for identity fingerprinting, then authentication accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant behavioral features from raw interaction data to create the identity fingerprint. Instead of processing and storing all possible behavioral data, the system identifies and extracts key discriminative features such as typing rhythm, cursor movement patterns, and navigation sequences, reducing data processing complexity while maintaining high verification accuracy.
Data Source
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AI summary
Multi-factor identity fingerprinting with user behavior is disclosed. A user's interactions with one or more parties are tracked and stored in a data store. A party may be a company itself or a company's information system. The user interactions are aggregated in a user profile bound to a particular user. All of the profile, or some subset of the profile may be used to generate an identity fingerprint. The identity fingerprint may be used as authentication credentials, where the similarity of user activity indicia is measured against all or part of the identity fingerprint. Alternatively, the aggregation systems may identify groups or categories of users by behavior by identifying similar identity fingerprints. Similarity may be measured via correlation models. Finally, the data store of profiles may be used for non-authentication systems such as business intelligence, advertising, identity management, and threat monitoring.