Proxy-Based Web Application Security with ML Classification
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Solution Overview
Problem
Traditional web application firewalls (WAFs) face challenges in effectively balancing user experience and security due to high false positives, complexity in maintenance, and inefficiencies in identifying new and general attack types, leading to performance issues and increased difficulty in distinguishing legitimate from malicious traffic.
Innovation Solution
A system utilizing a proxy with classification mechanisms, including a list of known clients and a model-based WAF that generates a classification model based on request attributes to differentiate between malicious and non-malicious requests, reducing false positives and improving maintenance efficiency by using supervised learning and active learning to update the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature based WAFs are deployed to protect web applications, then security protection is provided, but false positives increase and user experience deteriorates
Solution Approach 1:
The system transitions from static signature-based detection to dynamic machine learning models that adapt to new attack patterns. The WAF uses trained classification models that continuously learn from traffic patterns, enabling it to distinguish legitimate traffic from malicious traffic more accurately without relying on pre-defined signatures, thereby reducing false positives while maintaining security protection.
Solution Approach 2:
The system changes the detection parameters from fixed signature patterns to probabilistic classifications based on multiple traffic attributes. By analyzing multiple parameters simultaneously (headers, body, behavior patterns) and using machine learning to weigh them, the system achieves more accurate malicious traffic identification with fewer false positives compared to traditional single-parameter signature matching.
2Adaptability or versatility
If signature based WAF rules are increased to cover more threats, then security coverage is improved, but system performance deteriorates due to computational expense
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models offline using extensive attack and legitimate traffic data. The classification models are trained in advance to recognize attack patterns, so during runtime, the WAF can quickly classify new traffic using the pre-learned models without executing computationally expensive signature matching for every request, thus maintaining both security coverage and system performance.
Solution Approach 2:
The system replaces the mechanical signature matching process with machine learning-based classification. Instead of executing regular expression patterns for each request, the WAF uses trained models that have already learned attack characteristics, substituting the computationally intensive mechanical process with a more efficient intelligent classification system that provides comparable or better security coverage.
3Measurement precision
If profiling WAF approaches are used to learn traffic patterns, then detection accuracy for known vulnerabilities is improved, but adaptability to new attack types deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the WAF continuously monitors traffic patterns and uses this information to update and retrain classification models. By incorporating feedback from detected attacks and legitimate traffic, the system can adapt to new attack types while maintaining detection accuracy for known vulnerabilities, overcoming the limitation of static profiling approaches.
Solution Approach 2:
The system employs dynamic machine learning models that can adapt to changing traffic patterns and new attack types, unlike static profiling approaches. The models are designed to learn from new data continuously, enabling the WAF to maintain high detection accuracy for known vulnerabilities while simultaneously adapting to emerging threats and new attack vectors.
4Reliability
If traditional WAFs are deployed to filter malicious traffic, then security protection is provided, but manual intervention requirements increase due to noisy alerting systems
Solution Approach 1:
The system enables self-service by using machine learning models to automatically classify and filter malicious traffic without requiring constant manual review. The WAF autonomously makes decisions about blocking or allowing traffic based on trained classification models, significantly reducing the need for manual intervention while maintaining security protection, thereby easing operational burden.
Data Source
AI summary
A system is configured for protecting web applications at a host by analyzing web application behavior to detect malicious client requests. Example embodiments described herein include a proxy configured to handle network traffic between a host and clients. The proxy includes two request classification mechanisms, first a list of known clients, malicious and non-malicious, for identifying known malicious and known non-malicious requests and second a web application firewall for determining a classification for unknown requests (e.g., not originating from a known client). The classification itself may be distributed. The proxy determines whether a request is known non-malicious, known malicious, or unknown. The proxy collects request attributes for the known malicious and known non-malicious requests for the generation of a model based on the attributes of the known requests. The proxy passes the unknown requests to the WAF for determining a classification based on their attributes using the model.