Authentication Resolution Model for Error Pattern Analysis
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Solution Overview
Problem
Conventional client account authentication and lockout policies often result in undue burden for clients who inadvertently submit multiple erroneous authentication submissions, leading to account lockouts that require time and expense for both clients and organizations.
Innovation Solution
A computer-implemented method constructs an authentication resolution model using artificial intelligence to identify error patterns in erroneous submissions, allowing for the recognition of authentication exceptions, warnings, and attacks, enabling targeted responses such as completing authentication, sending warnings, or facilitating lockouts, while updating the model based on new submissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional account lockout policy is applied after multiple erroneous authentication submissions, then account security is improved, but client convenience and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary analysis of authentication error patterns before applying lockout policies. By constructing an authentication resolution model that learns from historical erroneous submissions, the system can distinguish between inadvertent client errors and malicious attacks in advance, allowing it to apply appropriate responses (authentication completion, warnings, or lockouts) rather than automatically locking out after a fixed number of attempts.
2Reliability
If conventional account lockout policy is applied after multiple erroneous authentication submissions, then account security is improved, but time and expense for both client and organization increase
Solution Approach 1:
The authentication resolution model enables the system to automatically distinguish between benign client errors and malicious attacks without human intervention. When the model identifies an authentication exception (inadvertent client error), it automatically completes authentication without requiring client contact with help desk or organizational intervention, thereby eliminating authentication recovery time for legitimate clients while maintaining security against attacks.
3Measurement precision
If authentication resolution model with AI algorithms is constructed to distinguish client errors from attacks, then authentication accuracy is improved, but system complexity increases
Solution Approach 1:
The authentication resolution model serves multiple functions within a single system component: it learns from historical error patterns, classifies new authentication attempts as exceptions/warnings/attacks, and triggers appropriate responses. This multi-functionality is achieved through AI algorithms that can be integrated into existing authentication infrastructure, avoiding the need for separate specialized systems for each function.
4Productivity
If dynamic authentication management is implemented to reduce false lockouts, then client efficiency is improved, but vulnerability to third-party attacks may increase
Solution Approach 1:
The authentication resolution model continuously learns from authentication submissions by analyzing error patterns and updating its understanding of client behavior. This feedback mechanism allows the system to adapt to legitimate client needs while maintaining security, as the model refines its classification accuracy over time rather than using static rules that cannot respond to changing attack patterns or client behaviors.
Data Source
AI summary
Techniques are described with regard to client authentication management. An associated method includes constructing an authentication resolution model specific to a client based upon error patterns respectively included in a plurality of erroneous authentication submissions inconsistent with a proper authentication submission. The method further includes receiving, via an authentication interface, a new erroneous authentication submission inconsistent with the proper authentication submission. Responsive to determining that the new erroneous authentication submission corresponds to an authentication exception defined in the authentication resolution model, the method further includes completing authentication. Responsive to determining that the new erroneous authentication submission corresponds to an authentication warning defined in the authentication resolution model, the method further includes performing at least one client account warning protection activity. Responsive to determining that the new erroneous authentication submission corresponds to an authentication attack defined in the authentication resolution model, performing at least one client account attack protection activity.