Robust and explainable detection
How robust, transferable and explainable AI-based intrusion detection really is: adversarial attacks guided by XAI, transfer across networks, MLOps and quantum machine learning.
Tools & projects
People
Antonio PescapèFull Professor · Principal Investigator
Francesco CerasuoloPostdoc
Vincenzo SpadariPhD Student
Gabriele MangiacaprePhD Student
Alfredo NascitaAssistant Professor (RTDA)
Antonio MontieriTenure-Track Professor
Idio GuarinoAssistant Professor (Non-Tenure Track) - Alma Mater Studiorum University of Bologna
Domenico CiuonzoAssociate Professor
Ciro GuidaPostdoc - Now at Unversité Clermont Auvergne and Université de Strasbourg
Valerio PersicoAssociate Professor
Giuseppe AcetoAssociate Professor
Davide di MondaPostdoc
Publications
- Conference G. Mangiacapre, A. Nascita, F. Cerasuolo, A. Montieri, A. Pescapè, “When Explanations Help Attackers: XAI-Guided Adversarial Attacks to Network Intrusion Detection Systems”, 2026 IFIP Networking Conference (IFIP Networking), 2026.
- Journal F. Cerasuolo, G. Bovenzi, A. Pescapè, “Cross-network transferability of AI-based network intrusion detection systems in heterogeneous Internet of Things environments”, Computer Networks, 2026.
- Journal G. Bovenzi, I. Guarino, A. Pescapè, “Multimodal contrastive learning-based network intrusion detection”, Results in Engineering, 2026.
- Conference V. Spadari, A. Nascita, I. Guarino, D. Ciuonzo, A. Pescapè, “Explainable Quantum Machine Learning for IoMT Security: Evaluating Quantum Advantage”, 2026 IEEE International Conference on Smart Computing Workshops and Other Affiliated events (SmartComp Companion), 2026.
- Conference D. D’Ambrosio, A. Nascita, A. Montieri, A. Pescapè, “Trustworthy Network Intrusion Detection Systems for IoMT: Explainability and Optimization”, 2026 IFIP Networking Conference (IFIP Networking), 2026.
- Conference V. Spadari, I. Guarino, D. Ciuonzo, A. Pescapè, “Towards Network Intrusion Detection via Quantum Machine Learning: A Reality Check”, IEEE INFOCOM 2025 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2025.
- Journal F. Cerasuolo, G. Bovenzi, D. Ciuonzo, A. Pescapè, “Adaptable, incremental, and explainable network intrusion detection systems for internet of things”, Engineering Applications of Artificial Intelligence, 2025.
- Conference V. Spadari, F. Cerasuolo, G. Bovenzi, A. Pescapè, “An MLOps Framework for Explainable Network Intrusion Detection with MLflow”, 2024 IEEE Symposium on Computers and Communications (ISCC), 2024.
- Journal A. Nascita, G. Aceto, D. Ciuonzo, A. Montieri, V. Persico, A. Pescapè, “A Survey on Explainable Artificial Intelligence for Internet Traffic Classification and Prediction, and Intrusion Detection”, IEEE Communications Surveys & Tutorials, 2024.
- 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.
- Conference C. Guida, A. Nascita, A. Montieri, A. Pescapè, “Cross-Evaluation of Deep Learning-based Network Intrusion Detection Systems”, 2023 10th International Conference on Future Internet of Things and Cloud (FiCloud), 2023.
- 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 I. Guarino, G. Bovenzi, D. Di Monda, G. Aceto, D. Ciuonzo, A. Pescapè, “On the use of Machine Learning Approaches for the Early Classification in Network Intrusion Detection”, 2022 IEEE International Symposium on Measurements & Networking (M&N), 2022.
- 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.