Many Web Application Firewalls (WAFs) leverage the OWASP Core Rule Set (CRS) to block incoming malicious requests. The CRS consists of different sets of rules designed by domain experts to detect well-known web attack patterns. Both the set of rules and the weights used to combine them are manually defined, yielding four different default configurations of the CRS. In this work, we focus on the detection of SQL injection (SQLi) attacks, and show that the manual configurations of the CRS typically yield a suboptimal trade-off between detection and false alarm rates. Furthermore, we show that these configurations are not robust to adversarial SQLi attacks, i.e., carefully-crafted attacks that iteratively refine the malicious SQLi payload by querying the target WAF to bypass detection. To overcome these limitations, we propose (i) using machine learning to automate the selection of the set of rules to be combined along with their weights, i.e., customizing the CRS configuration based on the monitored web services; and (ii) leveraging adversarial training to significantly improve its robustness to adversarial SQLi manipulations. Our experiments, conducted using the well-known open-source ModSecurity WAF equipped with the CRS rules, show that our approach, named ModSec-AdvLearn, can (i) increase the detection rate up to 30%, while retaining negligible false alarm rates and discarding up to 50% of the CRS rules; and (ii) improve robustness against adversarial SQLi attacks up to 85%, marking a significant stride toward designing more effective and robustWAFs.We release our open-source code at https://github.com/pralab/modsec-advlearn.

Cybersecurity attacks are on the rise, and the current response is not effective enough. The need for a competent workforce, able to face attackers, is increasing. At the moment, the gap between academia and real-world skills is huge and academia cannot provide students with skills that match those of an attacker. To pass on these skills, teachers have to train students in scenarios as close as possible to real-world ones. Capture the Flag (CTF) competitions are a great tool to achieve this goal, since they encourage students to think as an attacker does, thus creating more awareness on the modalities and consequences of an attack. We describe our experience in running an educational activity on ethical hacking, which we proposed to computer science and computer engineering students. We organized seminars, outside formal classes, and provided online support on the hands-on part of the training. We delivered different types of exercises and held a final CTF competition. These activities resulted in growing a community of students and researchers interested in cybersecurity, and some of them have formed ZenHack, an official CTF team.