14 citations · 25 across the 4 of their papers we have counts for
4 papers
DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors
Christian Scano, Diego Soi, Angelo Sotgiu +5
Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that, despite claims, existing prob…
SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras +3
Machine learning-based anomaly detection systems are increasingly being adopted in 5G Core networks to monitor complex, high-volume traffic. However, most existing approaches are e…
ModSec-Learn: Boosting ModSecurity with Machine Learning
Christian Scano, Giuseppe Floris, Biagio Montaruli +7
ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects malicious requests by matching them agai…
ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
Giuseppe Floris, Christian Scano, Biagio Montaruli +7
Many Web Application Firewalls (WAFs) leverage the OWASP CRS to block incoming malicious requests. The CRS consists of different sets of rules designed by domain experts to detect…