21 citations · 53 across the 13 of their papers we have counts for
7 papers · 1 filter
On the (In)Security of Loading Machine Learning Models
Gabriele Digregorio, Marco Di Gennaro, Stefano Zanero +2
The rise of model sharing through frameworks and dedicated hubs makes Machine Learning significantly more accessible. Despite its benefits, loading shared models exposes users to u…
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
Marco Di Gennaro, Giovanni De Lucia, Stefano Longari +2
Federated Learning has emerged as a privacy-oriented alternative to centralized Machine Learning, enabling collaborative model training without direct data sharing. While extensive…
GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
Davide Maffiola, Stefano Longari, Michele Carminati +2
Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time dat…
CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment
Stefano Longari, Alessandro Pozone, Jessica Leoni +4
In the last decades, Cyber-physical Systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attac…
Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
Stefano Longari, Paolo Cerracchio, Michele Carminati +1
The security of modern vehicles has become increasingly important, with the controller area network (CAN) bus serving as a critical communication backbone for various Electronic Co…
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…