Adaptive Rate Limiting to Distinguish Malicious Requests
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Solution Overview
Problem
Static rate limiting techniques often mistakenly restrict legitimate users, fail to detect malicious traffic, and struggle with scalability and flexibility, leading to service disruptions and inefficiencies.
Innovation Solution
Implement a dynamic rate limiting system that uses an auxiliary recommendation system to generate recommendations on network traffic, applying encrypted metadata caching and adaptive rules to block or transmit requests based on historical and current traffic patterns, reducing physical wear on storage components and improving user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rate limiting is used to control request rates, then service capacity can be managed, but legitimate users are mistakenly restricted and malicious traffic is not detected
Solution Approach 1:
The patent implements dynamic rate limiting by transitioning from static predefined thresholds to adaptive thresholds that automatically adjust based on real-time network conditions, historical traffic patterns, and learned behavior models. The system continuously monitors traffic characteristics and modifies rate limiting parameters dynamically to accurately distinguish between legitimate and malicious traffic while adapting to changing attack patterns and normal usage behaviors.
Solution Approach 2:
The system incorporates feedback mechanisms where the outcomes of rate limiting decisions are continuously monitored and fed back into the learning model. This feedback loop enables the system to learn from both false positives (blocking legitimate traffic) and false negatives (allowing malicious traffic), progressively improving classification accuracy. The feedback includes traffic pattern analysis, user behavior data, and security event information that refines future rate limiting decisions.
2Measurement precision
If static rate limiting rules are applied, then implementation is simple, but the system lacks scalability and precision
Solution Approach 1:
The rate limiting system performs self-service through automated learning and adaptation without requiring manual configuration or intervention. The system automatically collects traffic data, learns patterns through machine learning models, adjusts rate limiting thresholds autonomously, and optimizes its performance over time. This self-service capability enables high precision rate limiting while managing complexity through automation rather than manual rule management.
Solution Approach 2:
The system dynamically changes multiple parameters including rate limiting thresholds, time window durations, request rate limits, and classification sensitivity levels based on real-time network conditions and learned traffic patterns. These parameter changes enable precise adaptation to different traffic scenarios, attack types, and service requirements, achieving high measurement precision through continuous parameter optimization rather than fixed static values.
3Reliability
If rate limiting is increased to block more malicious traffic, then security improves, but service disruptions and user experience deteriorate
Solution Approach 1:
The system applies different rate limiting strategies and thresholds to different users, traffic types, time periods, and service endpoints based on their specific characteristics and risk profiles. Instead of uniform rate limiting, the system customizes protection levels locally for each context - applying stricter limits to suspicious traffic patterns while maintaining lenient limits for verified legitimate users, thereby preserving user experience while enhancing security where needed.
Solution Approach 2:
The system dynamically adjusts the strictness of rate limiting in real-time based on the detected threat level and traffic characteristics. When malicious activity is detected, the system temporarily increases blocking intensity locally for affected traffic sources. When traffic patterns indicate normal usage, the system relaxes limits to maintain smooth user experience. This dynamic adjustment ensures security improvements without permanent degradation of service quality for legitimate users.
Data Source
AI summary
The technology described herein relates to systems, methods, and computer storage media, among other things, for generating recommendations corresponding to whether requests (e.g., transmitted by computing devices) are malicious network traffic. For example, the recommendations can be generated using various historical and current network traffic trends (e.g., associated with a particular application programming interface). As another example, the recommendations can be generated based on particular models. Based on the generated recommendation, dynamic rate limiting rules can be applied for determining whether a request is malicious network traffic. Based on determining the request is malicious network traffic, the request (and additional requests associated with that particular request) can be blocked.