Machine Learning Authentication Risk Scoring
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Solution Overview
Problem
Existing online user account security systems often inconvenience legitimate users with additional authentication processes, leading to decreased user engagement and failing to effectively deter third-party account hijacking.
Innovation Solution
A machine learning-powered authentication challenge system that analyzes login features to predict the likelihood of malicious attempts, issuing targeted authentication challenges only to high-risk login attempts, thereby reducing unnecessary inconvenience for legitimate users and enhancing account security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional authentication processes are required for every login attempt, then account security is improved, but user experience deteriorates due to inconvenience and decreased engagement
Solution Approach 1:
The patent applies different authentication requirements to different login attempts based on their risk characteristics. Instead of uniformly requiring authentication challenges for all logins, the system analyzes specific features of each login attempt (such as location, device, time patterns) and selectively applies authentication challenges only to high-risk attempts. This localized approach maintains security for suspicious logins while preserving smooth access for legitimate users.
Solution Approach 2:
The system dynamically adjusts authentication requirements by changing parameters such as the threshold for triggering challenges, the types of challenges presented, and the analysis weights for different login features. By modifying these parameters based on real-time risk assessment, the system optimizes the balance between security enforcement and user convenience without requiring fixed, rigid authentication rules.
2Reliability
If authentication challenges are issued to all login attempts, then malicious attempts are deterred, but legitimate users experience unnecessary inconvenience
Solution Approach 1:
Rather than applying authentication challenges to all login attempts (excessive action), the system applies them only to a subset of high-risk attempts identified through machine learning analysis. This partial action approach ensures that security measures are applied sufficiently to deter malicious actors while avoiding the waste of user time on low-risk, legitimate login attempts where challenges would be unnecessary.
3Measurement precision
If machine learning analysis is applied to login attempts, then authentication challenges can be targeted accurately, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that sits between login attempt detection and authentication challenge issuance. This intermediary analyzes login features and predicts maliciousness probabilities, enabling accurate risk assessment without requiring complex rule-based logic throughout the entire system. The machine learning model encapsulates the complexity in a manageable, trainable component that can be updated independently.
Data Source
AI summary
Systems and methods are disclosed for automatically predicting a risk score of a user login attempt by receiving a user login attempt and generating a login feature vector associated with the user login attempt. The systems and methods further train a machine learning technique to establish a relationship between the login feature vector and the risk score. The trained machine learning technique is applied to new user login attempts to predict a risk score associated with the login attempt and issue an authentication challenge to the user if the risk score exceeds a predetermined threshold value.