ML WAF Attack Vector Detection via Feature Contribution Clustering
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Solution Overview
Problem
Existing web application firewalls (WAFs) face challenges in rapidly responding to new and evolving threats, as traditional rules-based WAFs require manual modifications and machine learning (ML) WAFs may miss minor variations in attack vectors.
Innovation Solution
A processing device analyzes attacks detected by an ML WAF to identify those not recognized by a rule-based WAF, determines feature contribution data, and uses clustering algorithms to group these attacks. Based on this analysis, new attack vectors and security vulnerabilities are identified, and corresponding rules are generated for the rule-based WAF to block these threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rules-based WAF is used to detect attacks, then the WAF can block known attack patterns, but it cannot rapidly respond to new and evolving threats without manual rule modifications
Solution Approach 1:
The system enables self-service by automatically generating new security rules through machine learning analysis. The ML model analyzes attack patterns and autonomously creates updated rules without requiring manual intervention from security experts, allowing the WAF to rapidly adapt to new threats while maintaining reliable detection
Solution Approach 2:
The system implements feedback by continuously analyzing attack detection results and using the ML model to generate improved rules. The automatic rule generation process creates a closed-loop system where detection outcomes feed back into rule refinement, enabling continuous improvement of threat response capabilities
2Adaptability or versatility
If machine learning WAF is used to detect attacks, then the WAF can identify complex attack patterns, but it may miss minor variations in attack vectors
Solution Approach 1:
The system merges the strengths of both approaches by combining ML-based detection with automatically generated rules. The hybrid architecture uses ML to identify complex patterns while the generated rules provide precise matching for specific attack vectors, ensuring both versatility and precision in threat detection
Solution Approach 2:
The WAF employs a composite detection mechanism that integrates multiple detection strategies. The system combines probabilistic ML analysis with deterministic rule-based matching, creating a composite security approach that leverages the adaptability of ML while maintaining the precision of rule-based detection for varied attack vectors
3Reliability
If manual rule modifications are made to respond to new threats, then the WAF can maintain high detection accuracy, but the process is time-consuming and requires security experts
Solution Approach 1:
The system eliminates the need for manual rule modification by implementing self-service through automated rule generation. The ML model automatically analyzes new attack patterns and generates appropriate security rules, removing the time-consuming manual process while maintaining high detection accuracy through intelligent analysis
Solution Approach 2:
The system performs preliminary action by proactively analyzing attack patterns and generating rules before threats can fully propagate. The automated rule generation process acts in advance to prepare defensive measures, reducing the time lag between threat emergence and WAF response
Data Source
AI summary
Embodiments of the present disclosure relate to detecting new attack vectors in web application servers based on analyzing requests (e.g., HTTP/S requests) that were flagged as attacks by a machine learning web application firewall (ML WAF) but not by a rule-based WAF. Such requests may be grouped together using a clustering algorithm, and the features that are determined as being high contributors to an overall attack probability in a threshold number of such requests may be used to determine new attack vectors.