AI-Driven WAF Policy Generation for Dynamic Web Security
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Solution Overview
Problem
Current web application firewall (WAF) configuration methods are inefficient and prone to human error, requiring advanced knowledge of attack patterns and being unable to adapt quickly to dynamic changes in web applications, leading to outdated and flawed security configurations.
Innovation Solution
A method involving continuous receipt and enrichment of network traffic requests, periodic analysis to generate network traffic rules, and automatic configuration of WAFs, utilizing data sources for real-time threat intelligence and geolocation updates to dynamically adjust security policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually configuring WAF policies is used, then security coverage for known attack patterns is achieved, but configuration time and human error increase
Solution Approach 1:
The system enables self-service configuration by automatically generating WAF policies through AI analysis of web application traffic patterns and attack vectors. The AI model processes input data about the web application and autonomously creates security policies without requiring manual configuration, thereby reducing configuration time while maintaining comprehensive security coverage.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with an automated AI-based system. Instead of human operators manually creating and updating WAF policies, the system uses machine learning models to automatically analyze traffic patterns, identify security risks, and generate appropriate policies, substituting human expertise with automated intelligent systems.
2Stability of the object's composition
If static WAF policies are enforced, then configuration stability is maintained, but adaptability to dynamic web application changes is lost
Solution Approach 1:
The system transitions from static to dynamic policy enforcement by continuously monitoring web application traffic patterns and automatically updating WAF policies in real-time. The AI model analyzes changing traffic behaviors and adapts security policies dynamically, allowing the system to respond to new attack vectors and application changes without requiring manual reconfiguration.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the effectiveness of security policies and adjusts them based on observed traffic patterns and attack success rates. This closed-loop feedback allows the system to learn from actual security events and automatically refine its policies, maintaining both stability through consistent enforcement and adaptability through continuous improvement.
3Ease of manufacture
If crawling web applications to generate policies is used, then policy generation capability is achieved, but response time to capture application changes increases
Solution Approach 1:
The system maintains continuous monitoring of web application traffic patterns rather than performing periodic crawling. The AI model continuously processes incoming traffic data to identify security risks and generate policies in real-time, eliminating the delays associated with batch crawling processes and enabling immediate response to application changes or new attack patterns.
Data Source
AI summary
A system and method for configuring a web application firewall (WAF) is provided. The method includes continuously receiving requests related to a first WAF, each request indicative of network traffic directed to a web application protected by the WAF; enriching each received request by associating each event with information from an enrichment source; periodically analyzing the enriched requests; generating at least one network traffic rule based on periodically generated analysis; and configuring at least a second WAF to perform the network traffic rule.