Multi-Factor Authentication Vectors for Fast Distance Matching
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Solution Overview
Problem
Existing multi-factor authentication methods face challenges in balancing security and convenience, often requiring significant computing resources and time, while less secure methods compromise on security.
Innovation Solution
A computing system uses vector computation to generate n-dimensional authenticated and MFA vectors from normalized inputs, determining the distance between them to authenticate multi-factor inputs, with machine learning models adjusting weights and thresholds for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more secure forms of authentication are implemented, then security is improved, but computing resources and time are significantly increased
Solution Approach 1:
The authentication process is divided into multiple independent factors (something you know, something you have, something you are) that are processed separately and then combined. Each factor is evaluated independently through vector generation and comparison, allowing parallel processing that improves speed while maintaining comprehensive security checks across all factors
Solution Approach 2:
The system transforms authentication data into vector representations with configurable dimensions and applies adjustable distance thresholds. Machine learning models dynamically optimize these parameters (vector dimensionality, threshold values, weighting schemes) to achieve the best balance between security accuracy and processing efficiency for different authentication scenarios
2Productivity
If traditional authentication methods are used, then computing resources are reduced, but false negatives increase and security is compromised
Solution Approach 1:
The system replaces traditional string-matching or simple threshold-based authentication mechanisms with vector space modeling and distance-based comparison. This substitution enables more nuanced similarity assessment that reduces false negatives while maintaining computational efficiency through optimized vector operations and distance calculations
Solution Approach 2:
The system employs machine learning models that automatically learn optimal authentication parameters from training data without requiring manual configuration. The models self-adjust weighting schemes, distance thresholds, and decision boundaries to minimize false negatives while maintaining security, reducing the need for expert tuning and ongoing manual optimization
Data Source
AI summary
Aspects of the disclosure relate to multi-factor authentication. A computing system may receive, from an authenticated entity, a plurality of authenticated inputs. Normalized authenticated inputs may be generated. An n-dimensional authenticated signature vector corresponding to the plurality of normalized authenticated inputs may be generated. A plurality of multi-factor authentication (MFA) inputs may be received. A plurality of normalized multi-factor authentication inputs may be generated. An n-dimensional multi-factor authentication vector corresponding to the plurality of normalized MFA input may be generated. There may be a determination of whether a distance between the n-dimensional authenticated signature vector and the multi-factor authentication vector not exceeding an authentication distance threshold. Based on the distance between the n-dimensional authenticated signature vector and the multi-factor authentication vector not exceeding the authentication distance threshold, an indication that the plurality of multi-factor authentication inputs have been authenticated may be generated.