Security Gateway Dynamic Exception Rules for False Positives
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Solution Overview
Problem
Current web application security systems face challenges in distinguishing between legitimate and malicious traffic, particularly with static whitelist models that become obsolete and generate false positives, and blacklist models that require frequent updates to prevent illegitimate traffic from passing through.
Innovation Solution
A security gateway that analyzes rejected messages, maintains frequency counts of attributes, and generates exception rules for attributes with high occurrence frequencies, allowing messages with similar attributes to pass through, thereby reducing false positives and improving security without constant updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a positive security model (whitelist) is used to enforce positive behavior, then security policy accuracy is improved, but false positives increase and legitimate messages are blocked
Solution Approach 1:
The patent transforms the static whitelist into a dynamic system that automatically adapts to legitimate traffic patterns. The security gateway learns from observed message frequencies and dynamically generates exception rules, allowing the whitelist to evolve without manual intervention while reducing false positives.
Solution Approach 2:
The system performs self-learning by automatically analyzing message frequencies and generating exception rules without human intervention. The security gateway autonomously identifies legitimate traffic patterns and creates appropriate exceptions, eliminating the need for manual rule updates.
2Ease of operation
If a negative security model (blacklist) is used to block recognized attacks, then ease of operation is improved, but security reliability deteriorates as new attacks pass through
Solution Approach 1:
The system performs preliminary learning during an initial phase where it observes and records message frequencies before enforcing security policies. This preliminary action builds a baseline of legitimate traffic patterns that enables the system to make informed security decisions afterward.
Solution Approach 2:
The system continuously monitors message frequencies and uses this feedback to dynamically generate exception rules. When legitimate traffic patterns are identified through frequency analysis, the system automatically adjusts its behavior to allow these messages while maintaining security against anomalies.
3Ease of manufacture
If a static whitelist is generated to allow legitimate actions, then security policy definition is simplified, but adaptability deteriorates when applications change
Solution Approach 1:
The patent transforms the static whitelist into a dynamic system that automatically adapts to legitimate traffic patterns. The security gateway learns from observed message frequencies and dynamically generates exception rules, allowing the whitelist to evolve without manual intervention while reducing false positives.
Solution Approach 2:
The system performs self-learning by automatically analyzing message frequencies and generating exception rules without human intervention. The security gateway autonomously identifies legitimate traffic patterns and creates appropriate exceptions, eliminating the need for manual rule updates.
Data Source
AI summary
A security gateway receives messages rejected by a message filter based on a set of rules. The security gateway also receives attributes of the rejected messages that triggered the rules. The security gateway maintains frequencies with which the messages with a particular attribute were rejected by the rules. The security gateway finds rejected messages or attributes having a high frequency of occurrence. Since messages or attributes 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 messages that have similar attributes to pass through the gateway.