Neural Network Authentication Rule Configuration
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Solution Overview
Problem
Existing methods for configuring user authentication rules on mobile devices are inefficient, as they often require user intervention and cannot accommodate the diverse range of mobile devices and security requirements, leading to either generic default rules that exclude newer or more secure authenticators or require users to manually select authentication components.
Innovation Solution
A neural network system automatically generates user authentication rules by processing historical data and user preferences without human input, using a rules recommender computer system connected to various data sources, including FIDO servers and user devices, to determine the optimal set of authentication components for each user based on predefined criteria and risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If default authentication rules are designated to cover all users, then compatibility with all mobile device types is improved, but newer or more secure authenticators are excluded
Solution Approach 1:
The system dynamically generates authentication rules based on user behavior patterns, device characteristics, and risk factors rather than using static default rules. This allows the system to adapt to each user's specific context and device capabilities, enabling both broad compatibility and high security standards to be achieved simultaneously.
Solution Approach 2:
The system changes multiple parameters including authentication method selection, confidence thresholds, and rule generation criteria based on analyzed user behavior and device data. This enables the system to optimize security levels for different user scenarios while maintaining compatibility across diverse mobile device types.
2Reliability
If users manually select authentication components, then authentication requirements are satisfied, but user intervention and system complexity increase
Solution Approach 1:
The system performs automatic rule generation and configuration without requiring user intervention. It analyzes user behavior patterns, device capabilities, and risk factors to self-determine the appropriate authentication rules, eliminating the need for users to manually select or configure authentication components while ensuring requirements are met.
Solution Approach 2:
The manual mechanical process of user selection and configuration is replaced with an automated computational system that uses machine learning and behavior analysis to generate authentication rules. This substitution eliminates user intervention while maintaining or improving authentication requirement satisfaction.
3Ease of operation
If automatic rule generation is implemented, then user intervention is eliminated, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary data collection and analysis during user onboarding and ongoing usage to build behavior profiles before authentication events occur. This preliminary action enables the automatic rule generation to operate with pre-processed data, reducing the computational complexity required during actual authentication operations.
Solution Approach 2:
The system introduces intermediate processing layers including behavior analysis modules, risk assessment components, and rule generation engines that mediate between raw data and final authentication decisions. These intermediaries organize and structure data processing in manageable stages, reducing overall system complexity while enabling automatic rule generation.
Data Source
AI summary
Systems and processes for automatically configuring user authentication rules for each of a plurality of users for use in transactions. A neural network engine receives first party preferences data from a first party computer that includes user authentication requirement criteria associated with a plurality of transaction types, and receives at least two of user behavior data, user historical data, authenticator data associated with a mobile device of the user, and mobile device metadata. The neural network engine then generates an output value based on this data, transmits the output value to a score comparator for comparison to a required score specified by the first party, and receives feedback data from the score comparator when the output value is not within a tolerance of the required score. When the output value is within the tolerance, then the neural network engine generates user authentication rules recommendations and transmits them to the first party computer.