Francesco Cerasuolo
I'm PhD Candidate in Computer Engineering
About
Francesco Cerasuolo is a Ph.D. candidate at the Department of Electrical Engineering and Information Technology of the University of Naples Federico II, where he is member of TRAFFIC research group. He received his Bachelor Degree in Computer Engineering in March 2019 and M.S. Laurea Degree in Computer Engineering in July 2022 from the same University. In September 2022, he started the Ph.D programme in Information Technology and Electrical Engineering at the University of Naples Federico II.
Ph.D. Candidate.
- Birthday: 22 February 1997
- Website: https://traffic.comics.unina.it/
- City: Naples, IT
- Age: 28
- Degree: Master
- Email: francesco.cerasuolo@unina.it
My current research interests lie in the areas of network systems, machine learning, and cyber-security. In particular, my main research activities focus on:
Resume
Sumary
Francesco Cerasuolo
Ph.D. Candidate@University of Naples Federico II.
- Via Claudio 21, 80125, Naples, Italy
- francesco.cerasuolo@unina.it
Education
Ph.D. in Computer Engineering
2022 - 2025
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione
University of Naples Federico II, IT
Thesis: Continuous and Adaptive Learning for Network Traffic Analysis in the New Internet Era
Master of Fine Arts & Graphic Design
2019 - 2022
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione
University of Naples Federico II, IT
Thesis: Class Incremental Learning in Deep Learning Mobile Traffic Classification
Bachelor of Computer Engineering
2015 - 2019
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione
University of Naples Federico II, IT
Thesis: Study and implementation of techniques for latency prediction in cloud-to-user networks
Experience
Undergraduate Researcher
2022 - 2022
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione
University of Naples Federico II, IT
Research topics addressed:
- Comparison and optimization of machine learning and deep learning models for the network security of mobile systems;
- Application of machine-based and deep learning approaches for analyzing the traffic of network attacks in "Internet of Things" scenarios;
- Prediction of video traffic generated by mobile applications through advanced multi-task deep learning approaches;
- Class-incremental learning techniques for the improvement of encrypted traffic classifiers generated by mobile apps.
Certification and Achievement
CCNA Routing and Switching: Routing and Switching Essentials
Cisco Networking Academy
CCNA Routing and Switching: Introduction To Networks
Cisco Networking Academy
Certificate of Attendance Salesforce Developer Bootcamp
University of Napoli Fedico II & Deloitte
Certificate of Attendance Apple Developer Academy
University of Napoli Fedico II & Apple
Publications
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R. Carillo, F. Cerasuolo, G. Bovenzi, D. Ciuonzo, A. Pescapé. (2025).
Explainable federated class incremental learning for Encrypted Network Traffic classification.
Computer Networks. -
F. Cerasuolo, G. Bovenzi, D. Ciuonzo, A. Pescapé. (2025).
Attack-adaptive network intrusion detection systems for IoT networks through class incremental learning.
Computer Networks. -
G. Bovenzi, F. Cerasuolo, D. Ciuonzo, D. Di Monda, I. Guarino, A. Montieri, V. Persico, A. Pescapé. (2025).
Mapping the landscape of generative AI in network monitoring and management.
IEEE Transactions on Network and Service Management. -
F. Cerasuolo, G. Bovenzi, D. Ciuonzo, A. Pescapè. (2025).
Adaptable, incremental, and explainable network intrusion detection systems for internet of things.
Engineering Applications of Artificial Intelligence. -
F. Cerasuolo, A. Nascita, G. Bovenzi, G. Aceto, D. Ciuonzo, A. Pescapè, D. Rossi. (2025).
MEMENTO: A novel approach for class incremental learning of encrypted traffic.
Computer Networks. - R. Carillo, F. Cerasuolo, A. Pescapè, E. Kanaki, P. Chatzimisios. (2025). Federated Incremental Learning for Encrypted Network Traffic Classification. Proceedings of the IEEE International Conference on Blockchain Computing and Applications (BCCA).
- F. Cerasuolo, G. Bovenzi, A. Montieri, A. Pescapè. (2025). Class Incremental Learning for Network-Agnostic Intrusion Detection Systems. Proceedings of the IEEE Research and Technologies for Society and Industry (RTSI).
- Francesco Cerasuolo, Idio Guarino, Vincenzo Spadari, Giuseppe Aceto, Antonio Pescapé. (2024). XAI for interpretable multimodal architectures with contextual input in mobile network traffic classification. Proceedings of the IFIP Networking Conference (IFIP Networking).
- F. Cerasuolo, G. Bovenzi, V. Spadari, D. Ciuonzo, A. Pescape. (2024). Explainable Few-Shot Class Incremental Learning for Mobile Network Traffic Classification. Proceedings of the IEEE Global Communications Conference (GLOBECOM).
- F. Cerasuolo, G. Bovenzi, C. Marescalco, F. Cirillo, D. Ciuonzo, A. Pescapè. (2023). Adaptive intrusion detection systems: Class incremental learning for IoT emerging threats. Proceedings of the IEEE International Conference on Big Data (BigData).
- A. Nascita, F. Cerasuolo, G. Aceto, D. Ciuonzo, V. Persico, A. Pescapé. (2023). Explainable mobile traffic classification: the case of incremental learning. Proceedings of the Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking (ACM CoNEXT).
- G. Bovenzi, F. Cerasuolo, A. Montieri, A. Nascita, V. Persico, A. Pescapé. (2023). A comparison of machine and deep learning models for detection and classification of android malware traffic. Proceedings of the IEEE Symposium on Computers and Communications (ISCC).
- L. Pappone, F. Cerasuolo, V. Persico, D. Ciuonzo, A. Pescapè, F. Esposito. (2022). Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning. Proceedings of the IFIP Networking Conference (IFIP Networking),
- A. Nascita, F. Cerasuolo, D. Di Monda, J.T.A. Garcia, A. Montieri, A. Pescapè. (2022). Machine and deep learning approaches for IoT attack classification. Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
- A. Rahman, T. Debnath, D. Kundu, F. Cerasuolo, Md. J. Islam, M. Rahman, M. Sayduzzaman, A. Pescapè (2024). Unlocking the Potential of IoT, AI, and Blockchain in Transforming Public and Private Industries. Springer.
Contact
At room 4.02
Department of Electrical Engineering and Information Technology (DIETI), Via Claudio, 21 - 80125 - Naples, Italy
Building 3/A, 4° floor, room 4.02.
Location:
Via Claudio 21, 80125, Naples, Campania, Italy
Email:
francesco.cerasuolo@unina.it