Authentication Notification Suppression Using Login Risk Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing authenticator app-based solutions overwhelm users with all-or-nothing notifications, leading to notification fatigue and increased vulnerability to social engineering attacks, as they do not analyze the context of login requests.
Innovation Solution
Suppress or throttle authentication request notifications based on heuristic and machine learning analysis of login context, such as IP address, location, and user history, and require manual access to the authenticator app for suspicious requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication request notifications are always displayed to users, then users can respond to all login attempts, but users experience notification fatigue and may accidentally approve fraudulent requests
Solution Approach 1:
The system applies different notification strategies based on the specific characteristics of each authentication request. By analyzing contextual factors such as device trust level, location, and user behavior patterns, the system selectively suppresses notifications for low-risk requests while maintaining notifications for high-risk requests, thereby reducing notification fatigue without compromising security.
Solution Approach 2:
The system dynamically adjusts the notification display parameter based on risk assessment. By changing the notification state from 'displayed' to 'suppressed' based on contextual parameter analysis (device trust score, geographic location, time patterns), the system optimizes the balance between security and user experience.
2Object-affected harmful factors
If authentication request notifications are suppressed for suspicious logins, then notification fatigue is reduced and social engineering attacks are mitigated, but legitimate login attempts may be incorrectly blocked
Solution Approach 1:
The system continuously monitors user feedback and authentication outcomes to refine its risk assessment models. By analyzing whether suppressed notifications were legitimate login attempts that users wanted to approve, the system adjusts its suppression algorithms to reduce false positives while maintaining false positive reduction for actual fraudulent attempts.
Solution Approach 2:
The system performs preliminary risk assessment and contextual analysis before suppressing notifications. By evaluating multiple contextual factors in advance (device trust level, geographic consistency, behavioral patterns), the system makes more accurate predictions about whether a login attempt is legitimate or fraudulent, reducing incorrect suppression of legitimate requests.
3Reliability
If all authentication requests require manual notification approval, then security is maximized, but user convenience and authentication speed are reduced
Solution Approach 1:
The system segments authentication requests into different risk categories and applies different handling strategies to each segment. High-risk requests require manual notification approval, while low-risk requests from trusted devices can proceed automatically or with reduced friction, thereby maintaining security without unnecessarily complicating legitimate user authentication.
Data Source
AI summary
Authentication request notifications are selectively suppressed, reducing notification fatigue and susceptibility to social engineering attacks. Authentication request notifications may be suppressed by not presenting a push notification on the user's phone. The authentication request may still be accessed and approved by manually opening the authenticator app. Notifications may be suppressed based on an estimation that the person attempting to login is not who they say they are. This estimation may be based on applying heuristics and/or machine learning models to the context of the login attempt, such as the IP address that originated the login request, time of day, recent user actions, patterns of previous logins, etc. One heuristic determines that the user has repeatedly ignored notifications caused by a particular IP address. Machine learning models generate a risk score from the login context, and notifications may be suppressed if the risk score exceeds a threshold.