User Confidence Score Calculation for Adaptive Device Authentication
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Solution Overview
Problem
Existing authentication methods, such as username and password-based systems or biometric records, are inadequate for effectively identifying device users in a rapidly evolving landscape of diverse devices accessing server devices and web applications.
Innovation Solution
A network traffic management system that retrieves user profile data from client devices, calculates a user confidence score based on baseline and current metrics, and compares this score against a threshold range to determine the authenticity of the user, enabling appropriate actions such as access permission or denial.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If username and password-based authentication or biometric records are used, then identification effectiveness is maintained, but adaptability to diverse devices deteriorates
Solution Approach 1:
The system creates a universal user profile that works across multiple device types (head devices with touchscreens and headless devices without direct input). The profile aggregates metrics from various sources including touchscreen interactions, movement data, and environmental sensors, making the authentication system adaptable to diverse device form factors while maintaining identification reliability through consistent behavioral analysis
Solution Approach 2:
The system changes the parameters used for identification from static credentials (username/password) or fixed biometric data to dynamic behavioral metrics that can be collected across different device types. By monitoring interactions, movement patterns, and environmental context, the system adapts to various devices while maintaining reliable user identification through continuous profile updates
2Reliability
If traditional authentication methods are used, then implementation simplicity is maintained, but security against fraud and bot attacks deteriorates
Solution Approach 1:
The system performs self-service by automatically collecting behavioral metrics, creating user profiles, and making authentication decisions without requiring complex manual configuration or intervention. The network traffic management device autonomously monitors interactions, calculates confidence scores, and executes access control decisions, reducing the need for complex security management while improving protection against fraud and bot attacks
3Measurement precision
If user profile data is continuously monitored and analyzed, then identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing behavioral metrics in user profiles during normal device operation. This pre-processing of data allows the system to quickly calculate confidence scores and make authentication decisions in real-time without excessive processing delays, as the foundational data is already aggregated and organized in the user profiles
Data Source
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AI summary
Methods, network traffic management devices, non-transitory computer readable media, and network traffic management systems that identify a device user based on a user-related metrics analysis include retrieving, in response to a received request requiring identification of a user at a client, user profile data associated with a user. A user confidence score is calculated based on a determined baseline user profile score for the user based on prior values associated with metrics in categories obtained from the retrieved user profile data and a determined current user profile score for the user based on current values associated with metrics in categories obtained from the retrieved user profile data. The calculated user confidence score is compared against a stored threshold range comprising minimum and maximum threshold scores. An action with respect to the user is executed in response to the request based on the comparison.