Network anomaly detection
Deep learning anomaly detection in IoT networks: fair benchmarking, robustness to data poisoning and interpretability through XAI.
Tools & projects
People
Antonio PescapèFull Professor · Principal Investigator
Valerio PersicoAssociate Professor
Giuseppe AcetoAssociate Professor
Domenico CiuonzoAssociate Professor
Antonio MontieriTenure-Track Professor
Alfredo NascitaAssistant Professor (RTDA)
Publications
- Conference A. Nascita, R. Carillo, F. Giampetraglia, A. Iacono, V. Persico, A. Pescapè, “Interpretability and Complexity Reduction in Iot Network Anomaly Detection Via XAI”, 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2024.
- Journal G. Bovenzi, G. Aceto, D. Ciuonzo, A. Montieri, V. Persico, A. Pescapè, “Network anomaly detection methods in IoT environments via deep learning: A Fair comparison of performance and robustness”, Computers & Security, 2023.
- Conference G. Bovenzi, A. Foggia, S. Santella, A. Testa, V. Persico, A. Pescapè, “Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis”, ICC 2022 - IEEE International Conference on Communications, 2022.