A Machine-Learning-Driven Evolutionary Approach for Testing Web Application Firewalls

摘要

Web application firewalls (WAFs) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs have to be updated and tested regularly to prevent attackers from easily circumventing them. In this paper, we focus on testing WAFs for SQL injection attacks, but the general principles and strategy we propose can be adapted to other contexts. We presentnML-Drivenn, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially,nML-Drivennautomatically generates a diverse set of attacks and submits them to the system being protected by the target WAF. Then,nML-Drivennselects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolves them to generate new successful bypassing attacks. Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF. We implementednML-Drivennin a tool and evaluated it on ModSecurity, a widely used open-source WAF, and a proprietary WAF protecting a financial institution. Our empirical results indicate thatnML-Drivennis effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns.