Login Risk Scoring for Real-Time Unauthorized Access Detection
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Solution Overview
Problem
Unauthorized logins pose a significant challenge in an increasingly connected environment, where compromised credentials can lead to unauthorized access and theft of sensitive information, with existing systems struggling to identify and mitigate such threats in real time.
Innovation Solution
A computer-implemented method using a machine learning model to generate scores based on login identification data, classify login requests, and take appropriate actions, including rejecting suspicious requests and prompting for multi-factor authentication, while continuously updating the model with feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional login authentication is used, then users can access services, but security against unauthorized logins is insufficient
Solution Approach 1:
The system performs preliminary analysis of login requests by generating risk scores before granting access. Machine learning models evaluate multiple factors (device information, login patterns, location data) in advance to identify potentially unauthorized logins, enabling preventive security measures rather than reactive responses.
Solution Approach 2:
The system implements continuous feedback loops where login outcomes and risk assessments are fed back into the machine learning models. This allows the system to learn from actual login patterns, refine risk scoring accuracy, and adapt to emerging unauthorized access techniques over time, progressively improving security detection capabilities.
2Measurement precision
If machine learning models are used to analyze all login requests, then unauthorized logins can be detected, but processing time and system complexity increase
Solution Approach 1:
The system applies partial machine learning analysis by generating risk scores for all login requests but performing full detailed classification only for requests exceeding specific risk thresholds. Low-risk logins receive expedited processing with minimal intervention, while high-risk logins trigger comprehensive analysis and additional verification steps, optimizing the balance between detection accuracy and processing speed.
3Reliability
If strict security measures are implemented, then unauthorized access is prevented, but user convenience and access speed are reduced
Solution Approach 1:
The system applies differentiated security measures based on local risk characteristics of each login request. Instead of uniform strict security for all users, the system tailors authentication requirements and monitoring intensity to the specific risk profile of each login attempt, applying enhanced security only where needed while maintaining convenience for low-risk legitimate users.
Data Source
AI summary
A computer-implemented method of identifying unauthorized logins may include: receiving a login request from a user device; using a machine learning model, generating a score corresponding to the login request, the machine learning model being trained to learn associations between identification data associated with login requests and scores based at least on (i) a set of prior login requests and (ii) a set of login classifications, each of the set of login classifications corresponding to at least one of the set of prior login requests; determining whether the score exceeds a predetermined score threshold; and in response to a determination that the score exceeds the predetermined score threshold, rejecting the login request and prompting a user of the user device to submit a renewed login request.