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Conference paper Open Access

Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach

Cascavilla Giuseppe; Johann Slabber; Fabio Palomba; Dario Di Nucci; Damian A. Tamburri; Willem-Jan van den Heuvel


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  <identifier identifierType="DOI">10.5281/zenodo.4534150</identifier>
  <creators>
    <creator>
      <creatorName>Cascavilla Giuseppe</creatorName>
      <affiliation>TU/e - JADS</affiliation>
    </creator>
    <creator>
      <creatorName>Johann Slabber</creatorName>
      <affiliation>Tilburg university - JADS</affiliation>
    </creator>
    <creator>
      <creatorName>Fabio Palomba</creatorName>
      <affiliation>SeSa Lab - University of Salerno Salerno, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Dario Di Nucci</creatorName>
      <affiliation>Tilburg University Den Bosch</affiliation>
    </creator>
    <creator>
      <creatorName>Damian A. Tamburri</creatorName>
      <affiliation>TU/e - JADS</affiliation>
    </creator>
    <creator>
      <creatorName>Willem-Jan van den Heuvel</creatorName>
      <affiliation>Tilburg University Den Bosch</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-09-28</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4534150</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4534149</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/787061</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/787061/">787061</awardNumber>
      <awardTitle>Advanced tools for fighting oNline Illegal TrAfficking</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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