Autori: M. A. Brignoli, S. Mazzaro, G. Fortunato, A. Corà, W. Matta, S. P. Romano, B. Ruggiero, V. Coscia

Editore: IEEE (Institute of Electrical and Electronics Engineers)

Tipologia Prodotto: Conference paper

Conferenza: 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT

DOI: 10.1109/MetroInd4.0IoT48571.2020.9138184

Numero prima e ultima pagina: 423 – 428

Codice ISBN Electronic: 978-1-7281-4892-2; USB: 978-1-7281-4891-5; Print on Demand (PoD): 978-1-7281-4893-9

Anno di pubblicazione: 2020



We present a framework able to combine exposure indicators and predictive analytics using AI-tools and big data architectures for threats detection inside a real industrial IoT sensors network. The described framework, able to fill the gaps between these two worlds, provides mechanisms to internally assess and evaluate products, services and share results without disclosing any sensitive and private information. We analyze the actual state of the art and a possible future research on top of a real case scenario implemented into a technological platform being developed under the H2020 ECHO project, for sharing and evaluating cybersecurity relevant informations, increasing trust and transparency among different stakeholders.

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