WAF-A-MoLE: Evading Web Application Firewalls through Adversarial Machine Learning

Abstract

Web Application Firewalls are widely used in production environments to mitigate security threats like SQL injections. Many industrial products rely on signature-based techniques, but machine learning approaches are becoming more and more popular. The main goal of an adversary is to craft semantically malicious payloads to bypass the syntactic analysis performed by a WAF. In this paper, we present WAF-A-MoLE, a tool that models the presence of an adversary. This tool leverages on a set of mutation operators that alter the syntax of a payload without affecting the original semantics. We evaluate the performance of the tool against existing WAFs, that we trained using our publicly available SQL query dataset. We show that WAF-A-MoLE bypasses all the considered machine learning based WAFs.

References in corpus (9)

• Scikit-learn: Machine Learning in Python
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• Evasion Attacks against Machine Learning at Test Time
• On the (Statistical) Detection of Adversarial Examples
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Cited by in corpus (4)

• RAT: Reinforcement-Learning-Driven and Adaptive Testing for Vulnerability Discovery in Web Application Firewalls
• AdvSQLi: Generating Adversarial SQL Injections against Real-world WAF-as-a-service
• ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
• Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors