Unsupervised Mobile Learning for Automated MFA Responses
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Solution Overview
Problem
Existing multi-factor authentication schemes enhance security but often compromise ease of access, necessitating user input for each authentication factor, which can be cumbersome and inefficient.
Innovation Solution
Implementing an unsupervised computer learning module on a mobile device to automate authentication decisions based on multiple parameters, including user and environmental inputs, without requiring user input for subsequent authentication requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-factor authentication procedures are implemented to enhance security, then security level is improved, but ease of operation deteriorates due to requiring additional user input for each authentication factor
Solution Approach 1:
The system performs authentication automatically without requiring user intervention. The mobile device autonomously receives authentication requests, processes them through the computer learning module, and sends responses back to the authentication server, eliminating the need for manual user input while maintaining security
Solution Approach 2:
The computer learning module is trained in advance with user authentication patterns and behaviors. This preliminary training enables the system to automatically recognize and respond to authentication requests based on pre-established criteria, resolving the contradiction between security and ease of operation
2Reliability
If user input is required for each authentication factor, then authentication security is maintained, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The mobile device automatically handles the entire authentication process including receiving requests, analyzing them through the trained computer learning module, and sending appropriate responses. This self-service mechanism eliminates manual user input while maintaining security standards, thereby improving authentication efficiency
Solution Approach 2:
The system continuously learns from authentication outcomes and user interactions. The computer learning module processes feedback from successful and failed authentication attempts to refine its decision-making algorithm, improving both security and efficiency over time without requiring additional user input
Data Source
AI summary
Techniques are disclosed relating to automating authentication decisions for a multi- factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request. The approval response is automatically generated and sent without receiving user input to automate the second request.