API Risk Evaluation via Multi-Dimensional Login Analysis
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Solution Overview
Problem
Conventional API security systems lack effective measures to detect and prevent unauthorized access, particularly from bogus devices, unusual activity bursts, anomalous behavior patterns, and suspicious geo-patterns, leading to potential false positives and security breaches.
Innovation Solution
An additional layer of protection is introduced through an Authentication/Risk Management system that monitors API login requests by comparing them against 'side information' from license and historical databases, including device types, usage patterns, and geographic restrictions, to identify inconsistencies and potential breaches, and implements remediation actions such as account lockdown and password expiration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional API security systems use basic login monitoring, then the system complexity remains low, but the ability to detect unauthorized access and anomalous behavior patterns is insufficient
Solution Approach 1:
The system segments security monitoring into multiple independent analysis modules: login attribute analysis, device fingerprinting, behavior pattern detection, geo-pattern analysis, and activity burst detection. Each module processes specific aspects of security data independently, improving detection accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces multiple analysis dimensions beyond basic login monitoring, including device fingerprints, behavior patterns, geographic locations, and temporal activity bursts. By adding these new dimensions of analysis, the system enhances its ability to detect unauthorized access without replacing the original login monitoring function.
2Reliability
If the system monitors multiple security attributes and implements comprehensive risk evaluation, then the security protection is enhanced, but the false positive rate increases due to overly sensitive detection
Solution Approach 1:
The system dynamically adjusts security thresholds and detection sensitivity based on contextual factors such as user profile, device trust level, geographic location, and historical behavior patterns. Rather than using fixed thresholds that cause false positives, the system adapts its detection criteria in real-time to balance security protection with accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously learn from detected patterns and adjust risk evaluation criteria. By analyzing confirmed security events and false alarms, the system refines its detection algorithms to reduce false positives while maintaining high security protection levels.
3Reliability
If the system implements comprehensive monitoring of device fingerprints, usage patterns, and geo-patterns, then the detection of anomalous behavior is improved, but the processing time and system response speed decrease
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing security attributes, device fingerprints, and behavior patterns in optimized data structures before actual security events occur. This pre-preparation allows the system to quickly match incoming login requests against pre-computed patterns, improving detection accuracy while reducing real-time processing time.
Solution Approach 2:
The system implements partial monitoring of all security attributes, focusing computational resources on the most critical and suspicious patterns rather than equally analyzing all possible attributes. By prioritizing high-value security checks and using selective monitoring strategies, the system maintains fast response speeds while providing comprehensive security protection.
Data Source
AI summary
A method, system and computer program product assess risk of an unauthorized API login and mitigate damage from an unauthorized API login. The method includes collecting in a database license attributes of a user license, user profile attributes, and database content change attributes; receiving an API login request; comparing features of the API login request to at least one of the database license attributes, user profile attributes, and database content change attributes against a predetermined threshold; assessing a risk of the an unauthorized API login request based on a result of the comparison; and based on a level of the assessed risk, implemented protective action to mitigate harm that may result from an unauthorized user from accessing information or services from a computer system by way of an API.