Mobile Login Authentication With Risk-Based PIN Verification
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Solution Overview
Problem
The issue of unauthorized access to login credentials on mobile devices, particularly in public spaces, poses a risk of fraud and unauthorized actions such as theft or password changes.
Innovation Solution
Implementing additional authentication methods, such as personal identification numbers (PINs) or passcodes, in conjunction with existing login credentials, and using a machine learning model to assess risk factors like geolocation, historical activity, and network usage to determine when extra authentication is necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional authentication (PIN/passcode) is required along with login credentials, then security against unauthorized access is improved, but user operation complexity increases
Solution Approach 1:
The system dynamically adjusts authentication requirements based on risk assessment. Machine learning models evaluate factors like geolocation, device fingerprint, and behavior patterns to determine when additional authentication is necessary. This dynamic approach maintains high security for risky logins while preserving ease of use for trusted access patterns.
Solution Approach 2:
The system changes authentication parameters adaptively. Instead of requiring additional authentication for all logins, the system modifies the authentication level based on evaluated parameters such as location consistency, time patterns, and device recognition. This allows the system to maintain security while reducing unnecessary operational complexity for legitimate users.
2Measurement precision
If machine learning models are used to assess risk factors, then authentication accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial machine learning assessment rather than full analysis for every login attempt. The ML model evaluates only the most relevant risk factors based on the specific context, such as geolocation changes or unfamiliar devices, rather than analyzing all possible parameters uniformly. This reduces computational overhead while maintaining authentication accuracy.
Solution Approach 2:
The system performs preliminary risk assessment using lightweight algorithms before triggering full machine learning analysis. Quick checks on basic factors like location and time patterns are performed first, and only when these indicate potential risk does the system engage more computationally intensive ML models. This staged approach optimizes resource usage while preserving detection accuracy.
Data Source
AI summary
Systems and methods for securing login authentication are disclosed. In one embodiment, a method may include: (1) receiving, by a mobile application executed by a mobile electronic device, a login from a user; (2) determining, by the mobile application, that secure login authentication is required; (3) prompting, by the mobile application, the user for additional authentication information comprising a personal identification number or a passcode; (4) receiving, by the mobile application, the additional authentication information; (5) comparing, by the mobile application, the additional authentication information to stored authentication information; and (6) presenting, by the mobile application, a landing screen in response to the additional authentication information matching the stored authentication information.