Machine Learning Take-Over Score for Account Security
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Solution Overview
Problem
Conventional network security systems face issues with insecurity and inaccuracy, leading to increased account take-over events (ATO) and system inefficiencies. These systems struggle to differentiate between legitimate user access and unauthorized access, even when two-factor authentication is circumvented.
Innovation Solution
The implementation of a digital security system that utilizes a machine-learning model to generate take-over scores based on client device features. This system intelligently provides dynamic access to account features by comparing take-over scores with predefined thresholds, allowing full access or limiting access to sensitive features accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication systems are used, then account access is granted based on credentials, but the system becomes vulnerable to account take-over events
Solution Approach 1:
The system performs preliminary analysis of device features and behavioral patterns before granting full account access. By evaluating multiple device characteristics in advance and generating risk scores, the system proactively identifies potential account take-over events before they can compromise sensitive operations
Solution Approach 2:
The system introduces an intermediary authentication layer that sits between credential verification and account access. This intermediary layer analyzes device features, compares them against stored profiles, and determines access permissions based on risk assessment, rather than directly granting access upon credential validation
2Measurement precision
If accurate authentication algorithms are implemented, then authorized users are correctly identified, but legitimate users are frequently mistaken for unauthorized devices
Solution Approach 1:
The system applies different evaluation criteria and threshold levels to different account features and operation types. Rather than using a single binary authentication decision, the system locally adjusts access permissions based on the specific operation being attempted, allowing authorized users to access low-risk features while blocking high-risk operations
Solution Approach 2:
The system implements partial access control by granting limited access to certain account features while restricting access to other features. Instead of completely blocking or fully granting access, the system applies selective restrictions based on the assessed level of risk for each specific operation or feature
3Reliability
If conventional systems lock accounts frequently, then security is maintained, but system operation is disrupted and computational resources are wasted
Solution Approach 1:
The system dynamically adjusts access permissions based on real-time risk assessment rather than applying static account locking. Access rights are continuously evaluated and modified based on the current operation, device characteristics, and risk score, allowing the system to remain flexible and responsive to changing conditions
Solution Approach 2:
The system segments account access into different levels and features rather than treating account access as a single binary state. By dividing account permissions into separate controllable units, the system can selectively restrict access to specific high-risk features while maintaining access to low-risk features, avoiding complete account lockouts
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine-learning models to determine take-over scores and intelligently provide or limit access to account features. In particular, in one or more embodiments, the disclosed systems can train and utilize digital security machine-learning models to generate a take-over score indicating a likelihood that the request to access the secure digital account is unauthorized activity. Based on the determining that the take-over score satisfies a take-over threshold, the disclosed systems can allow access to the secure digital account by providing secure account information but prohibit access to a subset of account features.