Adaptive Rate Limiting for AI-Based Authentication Traffic Differentiation
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Solution Overview
Problem
Existing DDOS attack mitigation strategies for authentication services are often static and network-focused, failing to differentiate between legitimate and malicious traffic, leading to inefficiencies and disruptions.
Innovation Solution
A system employing adaptive algorithms and machine learning models to dynamically adjust request rates based on behavioral analysis, distinguishing between legitimate and malicious authentication requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rate limiting is used to mitigate DDOS attacks, then system protection is improved, but legitimate traffic differentiation capability deteriorates
Solution Approach 1:
The patent implements dynamic rate limiting by transitioning from static threshold-based blocking to adaptive algorithms that continuously adjust rate limits based on real-time traffic analysis. The system monitors request patterns, identifies behavioral anomalies, and dynamically modifies rate limiting parameters to differentiate between legitimate and malicious traffic while maintaining system protection.
Solution Approach 2:
The system changes the parameter state by shifting from fixed rate limit values to dynamically adjusted parameters based on traffic conditions. The adaptive algorithms analyze multiple parameters including request frequency, source IP reputation, and behavioral patterns to continuously optimize rate limiting thresholds, enabling both protection and differentiation.
2Productivity
If network-focused mitigation strategies are employed, then attack volume reduction is improved, but service availability for legitimate users deteriorates
Solution Approach 1:
The patent segments traffic analysis into multiple dimensions including source IP reputation, request patterns, temporal characteristics, and behavioral heuristics. By dividing the mitigation approach into these segments, the system can selectively apply different rate limiting strategies to different traffic segments, reducing attacks while preserving legitimate service availability.
Solution Approach 2:
The system implements feedback loops where traffic analysis results continuously inform rate limiting decisions. The adaptive algorithms monitor the effectiveness of mitigation actions and adjust parameters in real-time based on observed outcomes, ensuring that attack reduction does not compromise legitimate user access.
3Measurement precision
If adaptive algorithms and machine learning are implemented, then traffic differentiation accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces intermediary components including heuristic analysis layers and machine learning models that act as mediators between raw traffic data and rate limiting decisions. These intermediaries process and interpret traffic patterns, translating complex analysis into actionable rate limiting parameters while managing system complexity through modular architecture.
Solution Approach 2:
The system implements self-service capabilities where the adaptive algorithms automatically train and optimize themselves using collected traffic data. The machine learning models continuously improve their differentiation accuracy through self-learning from labeled attack and legitimate traffic patterns, reducing the need for manual configuration and simplifying operational complexity.
Data Source
AI summary
A system, method, and device for adaptively limiting web requests is disclosed. The method includes (i) receiving a request at a proxy for an authentication service, (ii) determining whether the request is trusted based at least in part on a classification obtained from a classifier, and (iii) handling the request according to a determination of whether the request is trusted.