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
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.