Machine Learning Login Fraud Detection From Activity Patterns
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Solution Overview
Problem
Conventional authentication systems fail to utilize a user's pattern of activity logs across all channels and applications, making them vulnerable to fraud, as they rely on easily compromised factors like passwords and biometrics, and lack real-time fraud detection capabilities.
Innovation Solution
Implementing a platform and language agnostic machine learning model that generates a weighted score based on user biometrics and activity logs to detect login fraud in real-time, using a machine learning model trained with historical data across all channels and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication systems rely on passwords, biometrics, or device identifiers, then authentication can be performed, but the system becomes vulnerable to fraud as these factors can be easily compromised or stolen
Solution Approach 1:
The patent transforms authentication from static factor verification to dynamic behavioral pattern analysis. The machine learning model continuously analyzes user behavior parameters (typing patterns, mouse movements, navigation paths) and adapts authentication requirements based on detected anomalies, making fraud detection more reliable while reducing vulnerability to compromised credentials
Solution Approach 2:
The patent replaces mechanical authentication mechanisms (password entry, biometric scanning, device ID verification) with an intelligent system that observes and analyzes user interaction patterns. The machine learning model substitutes direct credential verification with indirect behavioral inference, detecting fraud through patterns rather than relying on easily compromised authentication factors
2Reliability
If authentication systems implement multiple authentication factors, then security is improved, but the complexity of the authentication process increases
Solution Approach 1:
The machine learning model performs authentication assessment automatically without requiring user intervention to select or provide multiple factors. The system self-evaluates risk levels based on behavioral patterns and autonomously determines authentication outcomes, eliminating the need for complex multi-factor workflows while maintaining security
Solution Approach 2:
The authentication system dynamically adjusts its requirements based on real-time behavioral analysis. Rather than statically requiring multiple factors for all logins, the system adapts authentication intensity to the detected risk level, simplifying the process for low-risk scenarios while maintaining security for high-risk attempts
3Reliability
If authentication systems do not utilize activity logs and historical user patterns, then the system is simpler to operate, but it cannot detect fraudulent login attempts that mimic legitimate user behavior
Solution Approach 1:
The system pre-establishes baseline behavioral patterns for each user by analyzing historical activity logs before authentication events occur. This preliminary profiling enables the machine learning model to quickly compare current login attempts against established patterns, detecting fraud without adding operational complexity during the authentication moment
Data Source
AI summary
Various methods, apparatuses/systems, and media for detecting login fraud based on a machine learning model are disclosed. A processor creates a machine learning model configured to be trained to generate a score based on user's biometrics data and a pattern of activity logs data of the user; trains the machine learning model with the user biometrics data and the pattern of activity logs data in real-time; receives user credentials data from the user for login attempt into a system; compares the received user credentials data with the biometrics data and the pattern of activity logs data of the user stored on a database and the machine learning model; and generates the score, in response to comparing, by utilizing the trained machine learning model. The score is a value that the processor compares with a predetermined threshold value to determine in real-time whether the login attempt is fraudulent.