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An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

1
Mossos d’Esquadra, Catalan Police, 08080 Barcelona, Spain
2
Department of Computer Science and Mathematics, Universitat Rovira i Virgili, CYBERCAT-Center for Cybersecurity Research of Catalonia, 43007 Tarragona, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 480; https://doi.org/10.3390/s19030480
Received: 30 December 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 24 January 2019
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things)
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Abstract

The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems. This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The proposed model allows us to study the rationality conditions of the automatic suspect detection technology, determining under which circumstances it is better than the traditional human-based approach. Our experimental results show the benefits of the proposed model. Compared to standard game theory, our ARA-based model indicates in general greater prudence in the deployment of the automatic technology and exhibits satisfactory performance without having to relax crucial hypotheses such as common knowledge and therefore subtracting realism from the problem, although at the expense of higher computational complexity. View Full-Text
Keywords: adversarial risk analysis; online surveillance; counterterrorism; threat identification; Internet of things adversarial risk analysis; online surveillance; counterterrorism; threat identification; Internet of things
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gil, C.; Parra-Arnau, J. An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance. Sensors 2019, 19, 480.

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