Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review


    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-2Engels
    TitelProceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020
    RedacteurenGenny Tortora, Giuliana Vitiello, Marco Winckler
    UitgeverijAssociation for Computing Machinery, Inc
    ISBN van elektronische versie9781450375351
    StatusGepubliceerd - 28 sep. 2020
    Evenement2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italië
    Duur: 28 sep. 20202 okt. 2020

    Publicatie series

    NaamACM International Conference Proceeding Series


    Congres2020 International Conference on Advanced Visual Interfaces, AVI 2020

    Bibliografische nota

    Publisher Copyright:
    © 2020 ACM.


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