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Cyber-Physical Behaviour Detection and Understanding using Artificial Intelligence.

Sabeur, Z., Bruno, A., Johnstone, L., Ferjani, M., Benaouda, D., Arbab-Zavar, B., Cetinkaya, D. and Sallal, M., 2022. Cyber-Physical Behaviour Detection and Understanding using Artificial Intelligence. In: 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022), 24-28 July 2022, New York, USA, 137-144.

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DOI: 10.54941/ahfe1002702


The advancement of cyber-physical behaviour detection and understanding in context of urban environment safety and security has been developed in the S4AllCities project (S4AllCities, 2020). Various concepts of fundamental artificial intelligence have been successfully implemented and subsequently tested in situ in some early S4AllCities pilot sites since early 2022. The detection of anomalies in cyber traffic communication protocols which take place in context of urban spaces have been investigated. But these were principally complemented with primary research on the intelligent detection of crowd physical behavior in the same urban spaces. The aim is to fuse both modes (cyber and physical) of detection for behavior deeper understanding. Indeed, this advances situation awareness for further native knowledge base reasoning as far as safety and security operations go across the urban space. Native knowledge concerns the evaluated risks and mitigation measures for responses to potential cyber-physical attacks on the urban space. In this study, the deployed artificial intelligence techniques established good benchmarks for classifying physical behavior under various scenarios of potential attacks. Our future work is to exercise the scalability, performance, evaluation, and validation of our intelligent algorithms which are fused together using in situ cyber and physical observation scenarios of the urban spaces under the final S4AllCities pilot sites in Bilbao, Spain

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Machine learning; Artificial intelligence; Intelligent agents; Crowd behavior; Computer vision
Group:Faculty of Science & Technology
ID Code:37288
Deposited By: Symplectic RT2
Deposited On:02 Aug 2022 07:53
Last Modified:02 Aug 2022 14:50


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