Tommaso Zoppi

Position: Researchers Zoppi, Tommaso
Email: This e-mail address is being protected from spambots. You need JavaScript enabled to view it
Phone or fax: +39 055 2751483
Location: Firenze

Tommaso Zoppi is currently a Research Associate at UNIFI, RCL Group.

He received his Bachelor and Master degree in Computer Science from the University of Firenze, Italy, in October 2012 and July 2014, respectively. From November 2014 to November 2017 he was a PhD student at the Matematics, Computer Science, Statistics course (curriculum: Computer Science) at the same university, under the supervision of Prof. Andrea Bondavalli. 

After almost two years of post-doc at the same university, Tommaso is now a Research Associate (RTD-A) at the Department of Mathematics and Informatics.

Despite his main research efforts are directed to anomaly detection and its applications to several domains related to critical systems, his research topics have a wide span:

  • anomaly-based intrusion detection
  • safety-critical architectures and V&V processes for the railway domain
  • applying machine learning in safety-critical systems
  • cyber-security analysis of smart grids
  • crisis management systems  


Dipartimento di Matematica e Informatica (DiMaI), Room T19 (Stanza Dottorandi)

Viale Morgagni, 65 - 50134 - Firenze Italy

CV Almalaurea

Recent Publications

  • Conference A. Muhammad, T. Zoppi, M. Gharib and A. Bondavalli. "Quantitative comparison of supervised algorithms and feature sets for traffic sign recognition". SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing. ACM ed. 2021. pp. 174-177. [More] 
  • Conference M. Gharib, T. Zoppi and A. Bondavalli. "Understanding the properness of incorporating machine learning algorithms in safety-critical systems". SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing. ACM ed. 2021. pp. 232-234. [More] 
  • Journal T. Zoppi, T. Capecchi, A. Ceccarelli and A. Bondavalli. "Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape", ACM Transactions on Data Science. 2021. [More] 
  • Journal T. Zoppi, A. Ceccarelli and A. Bondavalli. "Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application", IEEE Access, Vol. 9. 2021, pp. 90603-90615. [More] 
  • Journal T. Zoppi and A. Ceccarelli. "Prepare for trouble and make it double! Supervised – Unsupervised stacking for anomaly-based intrusion detection", Journal of Network and Computer Applications, Vol. 1, 9, 2021. [More] 

Resilient Computing Lab, 2011

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