Anti-impersonation via device-context and behavioral feature vectors
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Solution Overview
Problem
Current authentication mechanisms in web applications face challenges in effectively distinguishing between legitimate and impersonating users, particularly when attackers bypass context-related filters using VPNs or compromised machines, leading to false positives and security shortcomings.
Innovation Solution
The integration of device-context information and user behavior analysis using machine learning to generate feature vectors, comparing these against historical models to determine anomalies during user authentication sessions, thereby enhancing security without significantly impacting user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection is made too strict using device context profiling, then security detection accuracy is improved, but false positives increase and user experience deteriorates
Solution Approach 1:
The patent combines multiple authentication factors including device context information, behavioral biometrics, and traditional credentials into a unified risk assessment model. This multi-factor approach allows the system to achieve high detection accuracy without relying on overly strict single-factor rules, thereby reducing false positives while maintaining security.
Solution Approach 2:
The system dynamically adjusts detection parameters and risk thresholds based on the specific context of each authentication attempt. By changing parameters adaptively rather than using fixed strict thresholds, the system maintains high detection accuracy while accommodating legitimate variations in user behavior and device contexts.
2Reliability
If device context filtering is used to detect anomalies, then security against impersonation is improved, but attackers using VPNs or compromised machines can bypass the filters
Solution Approach 1:
The patent segments the authentication verification process into multiple independent analysis layers: device context analysis, behavioral biometric analysis, and risk assessment. This segmentation ensures that if attackers bypass one layer (e.g., device context filters), other layers (e.g., behavioral patterns) continue to provide security, making the system adaptable to various attack vectors.
Solution Approach 2:
The system creates a composite security model that integrates heterogeneous data sources including device hardware information, software environment data, network characteristics, and behavioral biometrics. This composite approach ensures that attackers cannot successfully bypass all filters simultaneously, as each data source provides complementary security coverage.
3Measurement precision
If behavioral biometrics are used for authentication, then security accuracy is improved, but monitoring time must be substantial which is impractical for log-in processes
Solution Approach 1:
The system performs preliminary collection of behavioral biometric data during the authentication process itself, rather than requiring extended monitoring periods. By capturing behavioral patterns (keystroke dynamics, mouse movements, navigation patterns) during the natural log-in interaction, the system achieves high authentication accuracy without adding substantial time requirements.
Solution Approach 2:
The patent enables rapid extraction and analysis of behavioral biometric features from brief interaction windows during authentication. By skipping the need for long-term behavioral monitoring and instead analyzing condensed behavioral signatures from the authentication moment, the system achieves both high accuracy and time efficiency.
Data Source
AI summary
Anti-impersonation techniques using device-context information and user behavior information from a session. The session can include a time period where a user of the client computer is performing an activity on the client computer (e.g., the session includes the user logging into an account online). The behavior information can include information on ways the user uses user input devices during the session. The device-context information can include HTTP session information. The techniques can include generating feature vector(s) for the received information, and comparing the feature vector(s) against model(s) of related historical information. The comparisons can provide level(s) of deviation of the feature vector(s) from the model(s). Also, the techniques can include determining whether the session is anomalous or normal according to the level(s) of deviation, and performing a security action in response to determining the session is anomalous.