Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).@en

Web application firewalls (WAF) 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 present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially, ML-Driven automatically generates a diverse set of attacks and submit them to the system being protected by the target WAF. Then, ML-Driven selects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolve 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 implemented ML-Driven in 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 that ML-Driven is effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns.@en