Marco Zuppelli
Orcid: 0000-0001-6932-3199
According to our database1,
Marco Zuppelli
authored at least 29 papers
between 2020 and 2024.
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Bibliography
2024
Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems.
J. Intell. Inf. Syst., August, 2024
Proceedings of the 32nd International Conference on Modeling, 2024
Dawn of LLM4Cyber: Current Solutions, Challenges, and New Perspectives in Harnessing LLMs for Cybersecurity.
Proceedings of the Ital-IA Intelligenza Artificiale, 2024
Erasing the Shadow: Sanitization of Images with Malicious Payloads Using Deep Autoencoders.
Proceedings of the Foundations of Intelligent Systems - 27th International Symposium, 2024
A Few to Unveil Them All: Leveraging Mixture of Experts on Minimal Data for Detecting Covert Channels in Containerized Cloud Infrastructures.
Proceedings of the IEEE European Symposium on Security and Privacy Workshops, 2024
No Country for Leaking Containers: Detecting Exfiltration of Secrets Through AI and Syscalls.
Proceedings of the 19th International Conference on Availability, Reliability and Security, 2024
Proceedings of the 19th International Conference on Availability, Reliability and Security, 2024
2023
SoftwareX, December, 2023
A federated approach for detecting data hidden in icons of mobile applications delivered via web and multiple stores.
Soc. Netw. Anal. Min., December, 2023
ORISHA: Improving Threat Detection through Orchestrated Information Sharing (Discussion Paper).
Proceedings of the 31st Symposium of Advanced Database Systems, 2023
Information Leakages of Docker Containers: Characterization and Mitigation Strategies.
Proceedings of the 9th IEEE International Conference on Network Softwarization, 2023
Using AI to face covert attacks in IoT and softwarized scenarios: challenges and opportunities.
Proceedings of the Italia Intelligenza Artificiale, 2023
2022
IEEE Trans. Netw. Serv. Manag., 2022
IPv6CC: IPv6 covert channels for testing networks against stegomalware and data exfiltration.
SoftwareX, 2022
Detection of Steganographic Threats Targeting Digital Images in Heterogeneous Ecosystems Through Machine Learning.
J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 2022
Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons.
Proceedings of the Pervasive Knowledge and Collective Intelligence on Web and Social Media, 2022
Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022
Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems.
Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022
Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022
Detection of Malicious Images in Production-Quality Scenarios with the SIMARGL Toolkit.
Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022
2021
An Effective and Efficient Approach to Improve Visibility Over Network Communications.
J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 2021
J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 2021
Kernel-level tracing for detecting stegomalware and covert channels in Linux environments.
Comput. Networks, 2021
Proceedings of the 7th IEEE International Conference on Network Softwarization, 2021
Proceedings of the Italian Conference on Cybersecurity, 2021
Proceedings of the Italian Conference on Cybersecurity, 2021
pcapStego: A Tool for Generating Traffic Traces for Experimenting with Network Covert Channels.
Proceedings of the ARES 2021: The 16th International Conference on Availability, 2021
Proceedings of the ARES 2021: The 16th International Conference on Availability, 2021
2020
Proceedings of the 6th IEEE Conference on Network Softwarization, 2020