HTTP Flood Protection via Fuzzy Logic and Trap Buffers
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Solution Overview
Problem
Current systems fail to effectively detect and mitigate HTTP flood attacks, which misuse Web server resources, particularly distributed and single-source IP HTTP page floods generated by HTTP bots, as they lack comprehensive real-time traffic analysis and adaptive protection mechanisms.
Innovation Solution
A server-based protection system utilizing a combination of fuzzy logic inference systems and statistical thresholds for anomaly detection, with trap buffers to characterize and mitigate attacks by analyzing rate-based and rate-invariant HTTP traffic parameters, and dynamically updating normal baseline values to adapt to changing traffic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time traffic analysis and adaptive protection mechanisms are implemented, then detection accuracy and mitigation effectiveness are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments traffic analysis into multiple independent modules: statistics module for collecting real-time parameters, learning module for computing baseline values, anomaly detection engine for generating anomaly degrees, and trap buffers for characterization. Each module processes specific aspects of traffic data independently, improving detection accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system implements dynamic adaptation by continuously updating normal baseline values through the learning module and adjusting detection sensitivity in real-time. The anomaly detection engine dynamically computes degree of anomaly values based on current traffic patterns, allowing the system to adapt to changing legitimate traffic behavior while maintaining accurate attack detection.
2Measurement precision
If multiple real-time statistical parameters are collected and analyzed, then anomaly detection capability is improved, but processing time and computational load increase
Solution Approach 1:
The learning module pre-computes normal baseline values for statistical parameters during periods of normal traffic operation. These pre-computed baselines are stored and ready for rapid comparison during anomaly detection, eliminating the need to re-analyze historical traffic patterns in real-time and significantly reducing processing time.
Solution Approach 2:
The system replaces complex real-time statistical analysis with fuzzy logic inference mechanisms. Instead of performing exhaustive statistical computations for each anomaly detection cycle, the system uses fuzzy logic to evaluate the degree of anomaly based on pre-computed baselines and real-time parameter comparisons, reducing computational load while maintaining detection precision.
3Manufacturing precision
If trap buffers are used to characterize attacks by source IP address, then mitigation accuracy is improved, but memory requirements and scalability decrease
Solution Approach 1:
The system implements local quality by creating separate trap buffers for different source IP addresses, allowing each buffer to store only the specific HTTP request patterns associated with that IP. This localized approach enables accurate attack characterization per source while limiting memory consumption to only the active attack patterns rather than storing all possible traffic data.
Solution Approach 2:
The system uses partial action by storing only the essential HTTP request pattern information in trap buffers rather than complete traffic data. The buffers store summarized characteristics of attacks (such as request types and patterns) rather than full packet data, reducing memory requirements while maintaining sufficient information for accurate mitigation.
4Adaptability or versatility
If adaptive mechanisms continuously update normal baseline values, then protection against evolving attacks is improved, but system stability and false positive reduction become more challenging
Solution Approach 1:
The learning module implements feedback mechanisms by continuously monitoring traffic patterns and adjusting normal baseline values based on observed behavior. This feedback loop allows the system to adapt to legitimate changes in traffic patterns over time while maintaining stable detection thresholds, as the baselines are updated incrementally rather than abruptly.
Solution Approach 2:
The system manages parameter changes by updating normal baseline values through controlled parameter adjustments in the learning module. The baselines are modified based on statistical significance and confidence thresholds, ensuring that changes reflect genuine pattern evolution rather than noise or temporary variations, thus maintaining system stability while achieving adaptability.
Data Source
AI summary
A system and method to detect and mitigate denial of service and distributed denial of service HTTP “page” flood attacks. Detection of attack/anomaly is made according to multiple traffic parameters including rate-based and rate-invariant parameters in both traffic directions. Prevention is done according to HTTP traffic parameters that are analyzed once a traffic anomaly is detected. This protection includes a differential adaptive mechanism that tunes the sensitivity of the anomaly detection engine. The decision engine is based on a combination between fuzzy logic inference systems and statistical thresholds. A “trap buffer” characterizes the attack to allow an accurate mitigation according to the source IP(s) and the HTTP request URL's that are used as part of the attack. Mitigation is controlled through a feedback mechanism that tunes the level of rate limit factors that are needed in order to mitigate the attack effectively while letting legitimate traffic to pass.