Skip to main content

Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces.

Sabeur, Z., Angelopoulos, C.M., Collick, L., Chechina, N., Cetinkaya, D. and Bruno, A., 2021. Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces. In: International Conference on Applied Human Factors and Ergonomics (AHFE 2021), 25-29 July 2021, USA (Virtual), 428 - 441.

Full text available as:

AHFE_Zoheir_Sabeur_Paper1846.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


Official URL:

DOI: 10.1007/978-3-030-80285-1_50


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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Conference proceedings published in Advances in Neuroergonomics and Cognitive Engineering. Book series: Lecture Notes in Networks and Systems. Springer International Publishing.
Uncontrolled Keywords:Internet of Things;· Artificial Intelligence; Edge Computing; Crowd Behavior; Digital Twins
Group:Faculty of Science & Technology
ID Code:35877
Deposited By: Symplectic RT2
Deposited On:06 Aug 2021 16:14
Last Modified:14 Mar 2022 14:29


Downloads per month over past year

More statistics for this item...
Repository Staff Only -