66 citations · 214 across the 24 of their papers we have counts for
13 papers · 1 filter
Quantum Sparse Recovery and Quantum Orthogonal Matching Pursuit
Armando Bellante, Stefano Vanerio, Stefano Zanero
We study quantum sparse recovery in non-orthogonal, overcomplete dictionaries: given coherent quantum access to a state and a dictionary of vectors, the goal is to reconstruct the…
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…
How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
Francesco Panebianco, Mario D'Onghia, Stefano Zanero aand Michele Carminati
Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigate…
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…
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…
PackHero: A Scalable Graph-based Approach for Efficient Packer Identification
Marco Di Gennaro, Mario D'Onghia, Mario Polino +2
Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: sign…
Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection
Marco Di Gennaro, Francesco Panebianco, Marco Pianta +2
Money laundering is a financial crime that poses a serious threat to financial integrity and social security. The growing number of transactions makes it necessary to use automatic…
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
Armando Bellante, Tommaso Fioravanti, Michele Carminati +2
Quantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are a…