most citedAmatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CR2025

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…

cs.CR2025

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…

cs.CR2025

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…

cs.CR20252 cited

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…

cs.CY2024

A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers

Diogo Reis Santos, Albert Sund Aillet, Antonio Boiano +10

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-makin…