Tommaso Zoppi

Position: Researchers Zoppi, Tommaso
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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

  • Journal T. Zoppi, A. Ceccarelli, L. Salani and A. Bondavalli. "On the educated selection of unsupervised algorithms via attacks and anomaly classes", Journal of Information Security and Applications, Vol. 52. 2020, pp. 102474. [More] 
  • Conference D. Bertieri, T. Zoppi, I. Mungiello, A. Ceccarelli, M. Barbareschi and A. Bondavalli. "Implementation, Verification and Validation of a Safe and Secure Communication Protocol for the Railway Domain". 9TH LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE COMPUTING. 2019. [More] 
  • Conference T. Zoppi, A. Ceccarelli and A. Bondavalli. "Evaluation of Anomaly Detection algorithms made easy with RELOAD". International Sympoosium on Software Reliability Engineering (ISSRE 2019). 2019. [More] 
  • Journal T. Zoppi, A. Ceccarelli and A. Bondavalli. "MADneSs: a Multi-layer Anomaly Detection Framework for Complex Dynamic Systems", IEEE Transactions on Dependable and Secure computing. 2019. [More] 
  • Conference . Falcao et al.. "Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection". Symposium on Applied Computing (SAC19) - DADS Track. 2019. [More] 

Resilient Computing Lab, 2011

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