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.
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