Guided Implicit Authentication via Contextual Behavior Analysis
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Solution Overview
Problem
The challenge lies in efficiently authenticating users on mobile Internet devices, where password entry is tedious and error-prone due to limited input interfaces, and single sign-on mechanisms do not adequately defend against device theft, as they only verify device identity rather than user identity.
Innovation Solution
A guided implicit authentication system that uses contextual data such as location, time, social network information, and communication data to determine user behavior patterns, allowing for password-less access when patterns are consistent and prompting further verification when anomalies are detected, with the option for user confirmation and input to adjust behavior measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If password entry is required for authentication, then security is improved, but ease of operation deteriorates due to tedious and error-prone password entry on mobile devices
Solution Approach 1:
The system performs authentication automatically by analyzing contextual data and user behavior patterns without requiring manual password entry. The authentication system serves itself by making intelligent decisions about whether to prompt for credentials or grant access based on contextual analysis, eliminating the need for users to manually enter passwords.
Solution Approach 2:
The patent replaces the mechanical/password-based authentication system with an automated contextual analysis system. Instead of relying on manual password entry, the system uses software-based behavioral analysis of contextual data (location, time, device usage patterns) to determine authentication, substituting the mechanical input process with automated intelligent decision-making.
2Ease of operation
If single sign-on is implemented to enable access to multiple applications, then ease of operation is improved, but security deteriorates because it does not defend against device theft
Solution Approach 1:
The system continuously monitors and analyzes contextual data and user behavior patterns, providing feedback about the current authentication risk level. Based on this feedback, the system dynamically adjusts its behavior - granting automatic access when risk is low and prompting for credentials when risk is high, thereby maintaining both ease of operation and security against device theft.
Solution Approach 2:
The authentication system transitions from static single sign-on to dynamic contextual-aware authentication. The system continuously adapts its authentication requirements based on real-time analysis of contextual data, device location, usage patterns, and potential anomalies, making the authentication process flexible and responsive to current conditions rather than fixed.
3Ease of operation
If implicit authentication based on contextual data is used, then ease of operation is improved by eliminating password entry, but measurement precision of user identity deteriorates due to potential false positives
Solution Approach 1:
The system applies partial authentication - it doesn't always require full explicit authentication, but rather uses contextual analysis to make partial authentication decisions. When contextual data strongly indicates legitimate user behavior, the system grants access without full authentication, improving ease of operation while maintaining sufficient precision through careful analysis of multiple contextual factors.
Solution Approach 2:
The system changes the authentication parameters from static credentials (passwords) to dynamic contextual parameters (location, time, behavior patterns). By analyzing multiple contextual parameters and their patterns over time, the system achieves both ease of operation and high measurement precision, as the contextual analysis can detect subtle changes in user behavior that indicate either legitimate access or potential theft.
Data Source
AI summary
Embodiments of the present disclosure provide a method and system for guided implicit authentication. The system first receives a request to access the controlled resource from a user. The system then determines whether the user request is inconsistent with regular user behavior by calculating a user behavior measure derived from historical contextual data of past user events. Next, the system allows the user to provide information associated with regular user behavior and/or current contextual data. The system further updates the user behavior measure based on current contextual data.