Passive-Dimension Access Verification for Adaptive Authentication
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Solution Overview
Problem
Existing multi-factor authentication systems burden users with frequent authentication requirements, leading to frustration and potential circumvention, thereby reducing security against unauthorized access.
Innovation Solution
A passive-dimension decision model evaluates user and device characteristics to determine an authentication challenge level, allowing seamless access for authorized users while flagging suspicious activities for supplemental authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-factor authentication is required for each access, then security against unauthorized access is improved, but user burden and frustration increase
Solution Approach 1:
The system dynamically adjusts authentication requirements based on risk assessment. Instead of requiring multi-factor authentication for every access attempt, the system evaluates each request in real-time and only challenges high-risk attempts, making the authentication process adaptive rather than static
Solution Approach 2:
The system performs passive authentication verification by automatically analyzing device characteristics, identity information, and access patterns without requiring active user participation. The authentication process serves itself by gathering necessary data from available sources and making access decisions autonomously
2Ease of operation
If passive authentication verification is implemented, then user burden is reduced and access efficiency is improved, but security against sophisticated attacks may be compromised
Solution Approach 1:
The system performs preliminary authentication verification by pre-establishing baselines of legitimate user behavior, device characteristics, and access patterns. This preliminary analysis is done in advance of actual access attempts, creating a reference framework for evaluating future requests
Solution Approach 2:
The system continuously monitors authentication outcomes and access patterns, using this feedback to refine its risk assessment algorithms. Successful and failed authentication attempts provide learning data that improves the system's ability to distinguish legitimate from malicious access over time
Data Source
AI summary
Aspects and examples are disclosed for improving multi-factor authentication techniques to control access to secured electronic resources. In one example, a decisioning computer system evaluates, based on a passive-dimension decision process, an access request, received from a user device, for a secured electronic resource. The passive-dimension decision process can evaluate dimensions associated with the access request, such as identity or device characteristics, to determine whether the dimensions of the access request are outside of norms for the user. Based on the passive-dimension decision model, the decisioning computing device may communicate to the user device an access decision, the access decision describing one or more of an access authorization, a denial of access, or a supplemental authentication challenge.