The ever-growing adoption of big data technologies, smart sensing, data science and artificial intelligence is enabling the development of new intelligent urban spaces with real-time monitoring and advanced cyber-physical situational awareness capabilities. In the S4AllCities international research project, the advancement of cyber-physical situational awareness will be experimented for achieving safer smart city spaces in Europe and beyond. The deployment of digital twins will lead to understanding real-time situation awareness and risks of potential physical and/or cyber-attacks on urban critical infrastructure specifically. The critical extraction of knowledge using digital twins, which ingest, process and fuse observation data and information, prior to machine reasoning is performed in S4AllCities. In this paper, a cyber behavior detection module, which identifies unusualness in cyber traffic networks is described. Also, a physical behaviour detection module is introduced. The two modules function within the so-called Malicious Attacks Information Detection System (MAIDS) digital twin.

Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces, 2021.

Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces

Alessandro Bruno
2021-01-01

Abstract

The ever-growing adoption of big data technologies, smart sensing, data science and artificial intelligence is enabling the development of new intelligent urban spaces with real-time monitoring and advanced cyber-physical situational awareness capabilities. In the S4AllCities international research project, the advancement of cyber-physical situational awareness will be experimented for achieving safer smart city spaces in Europe and beyond. The deployment of digital twins will lead to understanding real-time situation awareness and risks of potential physical and/or cyber-attacks on urban critical infrastructure specifically. The critical extraction of knowledge using digital twins, which ingest, process and fuse observation data and information, prior to machine reasoning is performed in S4AllCities. In this paper, a cyber behavior detection module, which identifies unusualness in cyber traffic networks is described. Also, a physical behaviour detection module is introduced. The two modules function within the so-called Malicious Attacks Information Detection System (MAIDS) digital twin.
Inglese
2021
https://link.springer.com/chapter/10.1007/978-3-030-80285-1_50#citeas
Advances in Neuroergonomics and Cognitive Engineering: Proceedings of the AHFE 2021 Virtual Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, July 25-29, 2021, USA
internazionale
contributo
Advances in Neuroergonomics and Cognitive Engineering
428
441
9783030802844
Switzerland
Springer, Cham
esperti anonimi
Online
Settore INF/01 - Informatica
   Smart Spaces Safety and Security for All Cities
   S4AllCities
   European Commission
   Horizon 2020 Framework Programme
   883522
6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10808/50164
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