6 citations · 10 across the 4 of their papers we have counts for
4 papers
Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
Gabriele Digregorio, Elisabetta Cainazzo, Stefano Longari +2
The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G c…
Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
Gabriele Digregorio, Francesco Bleggi, Federico Caroli +3
A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency…
Poster: libdebug, Build Your Own Debugger for a Better (Hello) World
Gabriele Digregorio, Roberto Alessandro Bertolini, Francesco Panebianco +1
Automated debugging, long pursued in a variety of fields from software engineering to cybersecurity, requires a framework that offers the building blocks for a programmable debuggi…
Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Gabriele Digregorio, Salvatore Maccarrone, Mario D'Onghia +4
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an e…