Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach

Giuseppe Cascavilla; Johann Slabber; Fabio Palomba; Dario Di Nucci; Damian A. Tamburri; Willem Jan Van Den Heuvel

doi:10.1145/3399715.3399826

Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach

Giuseppe Cascavilla, Johann Slabber, Fabio Palomba, Dario Di Nucci, Damian A. Tamburri, Willem Jan Van Den Heuvel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020
Editors	Genny Tortora, Giuliana Vitiello, Marco Winckler
Publisher	Association for Computing Machinery, Inc.
ISBN (Electronic)	9781450375351
DOIs	https://doi.org/10.1145/3399715.3399826
Publication status	Published - 28 Sept 2020
Event	2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italy Duration: 28 Sept 2020 → 2 Oct 2020

Conference

Conference	2020 International Conference on Advanced Visual Interfaces, AVI 2020
Country/Territory	Italy
City	Salerno
Period	28/09/20 → 2/10/20

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2020 ACM.

Funding

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).

Keywords

Computer Vision
Counterterrorism
Cyber-Physical Spaces
Generative Adversarial Neural Networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3399715.3399826

Cite this

Cascavilla, G., Slabber, J., Palomba, F., Di Nucci, D., Tamburri, D. A., & Van Den Heuvel, W. J. (2020). Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach. In G. Tortora, G. Vitiello, & M. Winckler (Eds.), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020 Association for Computing Machinery, Inc.. https://doi.org/10.1145/3399715.3399826

@inproceedings{8d29dd61041a4022aab836d11be25f3f,

title = "Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach",

abstract = "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. ",

keywords = "Computer Vision, Counterterrorism, Cyber-Physical Spaces, Generative Adversarial Neural Networks",

author = "Giuseppe Cascavilla and Johann Slabber and Fabio Palomba and {Di Nucci}, Dario and Tamburri, {Damian A.} and {Van Den Heuvel}, {Willem Jan}",

note = "Funding Information: This paper was funded by the European Union{\textquoteright}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). Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 International Conference on Advanced Visual Interfaces, AVI 2020 ; Conference date: 28-09-2020 Through 02-10-2020",

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day = "28",

doi = "10.1145/3399715.3399826",

language = "English",

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Cascavilla, G, Slabber, J, Palomba, F, Di Nucci, D , Tamburri, DA & Van Den Heuvel, WJ 2020, Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach. in G Tortora, G Vitiello & M Winckler (eds), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. Association for Computing Machinery, Inc., 2020 International Conference on Advanced Visual Interfaces, AVI 2020, Salerno, Italy, 28/09/20. https://doi.org/10.1145/3399715.3399826

Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach. / Cascavilla, Giuseppe; Slabber, Johann; Palomba, Fabio et al.
Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. ed. / Genny Tortora; Giuliana Vitiello; Marco Winckler. Association for Computing Machinery, Inc., 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Counterterrorism for Cyber-Physical Spaces

T2 - 2020 International Conference on Advanced Visual Interfaces, AVI 2020

AU - Cascavilla, Giuseppe

AU - Slabber, Johann

AU - Palomba, Fabio

AU - Di Nucci, Dario

AU - Tamburri, Damian A.

AU - Van Den Heuvel, Willem Jan

N1 - Funding Information: 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). Publisher Copyright: © 2020 ACM.

PY - 2020/9/28

Y1 - 2020/9/28

N2 - 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.

AB - 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.

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KW - Generative Adversarial Neural Networks

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M3 - Conference contribution

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BT - Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020

A2 - Tortora, Genny

A2 - Vitiello, Giuliana

A2 - Winckler, Marco

PB - Association for Computing Machinery, Inc.

Y2 - 28 September 2020 through 2 October 2020

ER -

Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach

Abstract

Conference

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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