Publications
Peer-reviewed journals, conference proceedings, and workshops
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MininetGym: A Live Demonstration of RL-Based Cybersecurity Training
Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), pp. 4086–4088 · ACM · Paphos, Cyprus
@inproceedings{finistrella2026mininetgym_demo,
title={MininetGym: A Live Demonstration of RL-Based Cybersecurity Training},
author={Finistrella, Salvo and Mariani, Stefano and Zambonelli, Franco},
booktitle={Proceedings of the 25th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2026)},
pages={4086--4088},
year={2026},
publisher={ACM},
address={Paphos, Cyprus},
doi={10.65109/VVUY3381}
}
Experiences in Exploiting Reinforcement Learning for Network Traffic Classification and Attack Detection
18th International Conference on Agents and Artificial Intelligence (ICAART 2026), Vol. 3, pp. 2563–2572 · SCITEPRESS · Marbella, Spain
@inproceedings{finistrella2026experiences,
title={Experiences in Exploiting Reinforcement Learning for Network
Traffic Classification and Attack Detection},
author={Finistrella, Salvo and Mariani, Stefano and Zambonelli, Franco},
booktitle={Proceedings of the 18th International Conference on
Agents and Artificial Intelligence (ICAART 2026)},
volume={3},
pages={2563--2572},
year={2026},
publisher={SCITEPRESS},
address={Marbella, Spain},
doi={10.5220/0014300900004052},
isbn={978-989-758-796-2}
}
MininetGym: A Modular SDN-Based Simulation Environment for Reinforcement Learning in Cybersecurity
SoftwareX, Vol. 31, Art. 102312 · Elsevier · ISSN 2352-7110
@article{finistrella2025mininetgym,
title={MininetGym: A modular SDN-based simulation environment for
reinforcement learning in cybersecurity},
author={Finistrella, Salvo and Mariani, Stefano and Zambonelli, Franco},
journal={SoftwareX},
volume={31},
pages={102312},
year={2025},
issn={2352-7110},
doi={10.1016/j.softx.2025.102312},
url={https://www.sciencedirect.com/science/article/pii/S235271102500278X},
publisher={Elsevier}
}
Multi-Agent Reinforcement Learning for Cybersecurity: Classification and Survey
Intelligent Systems with Applications, Vol. 26, Art. 200495 · Elsevier
@article{finistrella2025marl_survey,
title={Multi-Agent Reinforcement Learning for Cybersecurity:
Classification and Survey},
author={Finistrella, Salvo and Mariani, Stefano and Zambonelli, Franco},
journal={Intelligent Systems with Applications},
volume={26},
pages={200495},
year={2025},
doi={10.1016/j.iswa.2025.200495},
publisher={Elsevier}
}
Multi-Agent Reinforcement Learning for Cybersecurity: Approaches and Challenges
Proceedings of the 25th Workshop "From Objects to Agents" (WOA 2024), CEUR Workshop Proceedings Vol. 3735, pp. 103–118
@inproceedings{finistrella2024approaches,
title={Multi-Agent Reinforcement Learning for Cybersecurity:
Approaches and Challenges},
author={Finistrella, Salvo and Mariani, Stefano and Zambonelli, Franco},
booktitle={Proceedings of the 25th Workshop ``From Objects to Agents''
(WOA 2024)},
series={CEUR Workshop Proceedings},
volume={3735},
pages={103--118},
year={2024},
publisher={CEUR-WS.org}
}