6 papers
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
Gabriele Digregorio, Marco Di Gennaro, Francesco Pastore +3
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded with…
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…
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…