3 papers
cs.CR2025
Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Roberto Doriguzzi-Corin, Petr Sabel, Silvio Cretti +1
Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, wh…
cs.CR2025
Adaptive Federated Learning with Functional Encryption: A Comparison of Classical and Quantum-safe Options
Enrico Sorbera, Federica Zanetti, Giacomo Brandi +3
Federated Learning (FL) is a collaborative method for training machine learning models while preserving the confidentiality of the participants' training data. Nevertheless, FL is…
cs.CR2024
INTELLECT: Adapting Cyber Threat Detection to Heterogeneous Computing Environments
Simone Magnani, Liubov Nedoshivina, Roberto Doriguzzi-Corin +2
The widespread adoption of cloud computing, edge, and IoT has increased the attack surface for cyber threats. This is due to the large-scale deployment of often unsecured, heteroge…