Samenvatting
Simulating terrorist scenarios in cyber-physical spaces - -that is, urban open or (semi-) closed spaces combined with cyber-physical systems counterparts - -is challenging given the context and variables therein. This paper addresses the aforementioned issue with ALTer a framework featuring computer vision and Generative Adversarial Neural Networks (GANs) over terrorist scenarios. We obtained the data for the terrorist scenarios by creating a synthetic dataset, exploiting the Grand Theft Auto V (GTAV) videogame, and the Unreal Game Engine behind it, in combination with OpenStreetMap data. The results of the proposed approach show its feasibility to predict criminal activities in cyber-physical spaces. Moreover, the usage of our synthetic scenarios elicited from GTAV is promising in building datasets for cybersecurity and Cyber-Threat Intelligence (CTI) featuring simulated video gaming platforms. We learned that local authorities can simulate terrorist scenarios for their cities based on previous or related reference and this helps them in 3 ways: (1) better determine the necessary security measures; (2) better use the expertise of the authorities; (3) refine preparedness scenarios and drills for sensitive areas.
Originele taal-2 | Engels |
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Titel | Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020 |
Redacteuren | Genny Tortora, Giuliana Vitiello, Marco Winckler |
Uitgeverij | Association for Computing Machinery, Inc. |
ISBN van elektronische versie | 9781450375351 |
DOI's | |
Status | Gepubliceerd - 28 sep. 2020 |
Evenement | 2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italië Duur: 28 sep. 2020 → 2 okt. 2020 |
Congres
Congres | 2020 International Conference on Advanced Visual Interfaces, AVI 2020 |
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Land/Regio | Italië |
Stad | Salerno |
Periode | 28/09/20 → 2/10/20 |
Bibliografische nota
Publisher Copyright:© 2020 ACM.
Financiering
This paper was funded by the European Union’s Internal Security Fund — Police under grant agreement n° 815356. Palomba acknowledges the support of the Swiss National Science Foundation through the SNF Project No. PZ00P2_186090 (TED).