Adaptive Web Security Gateway with Dynamic Rule Learning
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Solution Overview
Problem
Current web application security models, such as positive and negative security models, face challenges in accurately distinguishing between legitimate and illegitimate traffic, leading to false positives and the need for static rule lists that become obsolete with application changes.
Innovation Solution
A security gateway system that uses an adaptive filter and learning engine to analyze rejected URL messages, storing them in a trie structure with frequency counts to generate exception rules for high-frequency attributes, allowing legitimate traffic to pass through by generalizing characteristics of descendant nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a positive security model is used to generate a whitelist of allowable actions, then legitimate traffic can be allowed, but the rule list becomes static and obsolete when the application changes, causing false positives
Solution Approach 1:
The patent transforms the static whitelist approach into a dynamic system that continuously learns from intercepted traffic. The learning engine automatically adapts the security rules based on observed application behavior, making the rule set evolve with the application without requiring manual updates. This resolves the contradiction by making the security model both reliable (through continuous learning) and adaptable (through automatic updates).
Solution Approach 2:
The system implements a feedback mechanism where the learning engine continuously monitors traffic patterns and uses this information to refine and update the whitelist rules. The system learns from actual usage patterns and adjusts its behavior accordingly, creating a closed-loop system that improves accuracy over time while adapting to application changes automatically.
2Reliability
If a negative security model is used to block recognized attacks, then security can be enforced, but the signature list must be constantly updated to handle new attacks, and legitimate traffic may be blocked
Solution Approach 1:
The learning engine operates autonomously to learn and adapt security rules without requiring manual intervention for signature updates. The system self-updates its rule set by analyzing traffic patterns and automatically adjusting its behavior, eliminating the need for continuous manual maintenance of signature databases while maintaining security enforcement.
3Reliability
If a static whitelist is generated for web application protection, then initial security coverage is achieved, but the rules become obsolete with application changes leading to false positives
Solution Approach 1:
The system performs preliminary learning during an initial phase to establish a baseline security model, then continuously adapts in real-time as the application evolves. This preliminary action provides immediate security coverage while the system simultaneously learns and adapts to application changes, eliminating the time lag associated with manual rule updates.
Solution Approach 2:
The learning engine operates continuously to maintain and update security rules, ensuring that protection remains effective as the application evolves. The system performs useful action (learning and adaptation) continuously rather than in discrete update cycles, maintaining both initial security coverage and ongoing adaptability without interruption.
Data Source
AI summary
A security gateway receives messages, such as URL requests, rejected by a message filter based on a set of rules. The security gateway maintains frequencies with which the messages were rejected by the rules. The security gateway finds rejected messages having a high frequency of occurrence. Since messages having a high frequency of occurrences are more likely to represent legitimate requests rather than malicious attacks, the security gateway generates exception rules, which would allow similar messages to pass through the gateway.