Alessandro Palma

Postdoctoral Researcher

SAPIENZA University of Rome

I am a postdoctoral researcher at Sapienza University of Rome, Italy. My research interests include automated and quantifiable cyber risk assessment, both from a technical perspective, researching attack modeling and graphs, and from a process perspective, providing decision support for security operations and how they can be integrated into autonomic cybersecurity (e.g., self-protecting systems).

I got a Ph.D. with Laude in Engineering in Computer Science at Sapienza University of Rome, Italy, where I also received my MSc in Engineering in Computer Science. In 2022 I worked as a research fellow with CINI Cyber Security National Laboratory on Open Source INTelligence (OSINT) topics.

During my Ph.D., I was a visiting scholar at Télécom SudParis - Institut Polytechnique de Paris.

CONTACTS

Email: palma@diag.uniroma1.it

Dipartimento di Ingegneria Informatica, Automatica e Gestionale "Antonio Ruberti" (DIAG)

Sapienza Università di Roma

Via Ariosto 25, 00185, Roma (Italy)

Room: B112

NEWS

The track "Applications and Systems for Healthcare" has been accepted at SAC 2026. Consider submitting your work.
The paper "Behind the scenes of attack graphs: Vulnerable network generator for in-depth experimental evaluation of attack graph scalability " has just been published in Elsevier Computers & Security
The paper "IMPAVID: Enhancing incident management process compliance assessment with visual analytics " has just been published in Elsevier Computers & Graphics
🏆 We won the Best Paper Award from SEAMS 2025 with the paper "SPARQ: A QoS-Aware Framework for Mitigating Cyber Risk in Self-Protecting IoT Systems"
On April 1, 2025, the paper "SHIELD: Assessing Security-by-Design in Federated Data Spaces Using Attack Graphs" with Nikolaos Papadakis, Georgios Bouloukakis, Joaquin Garcia-Alfaro, Mattia Sospetti, and Kostas Magoutis was presented at SAC 2025
🏆 We won the best paper runner-up award from ESORICS 2024 with the paper "It is Time To Steer: A Scalable Framework for Analysis-Driven Attack Graph Generation"

Report abuse