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Predicting the performance of users as human sensors of security threats in social media

Predicting the performance of users as human sensors of security threats in social media

Heartfield, Ryan and Loukas, George ORCID: 0000-0003-3559-5182 (2016) Predicting the performance of users as human sensors of security threats in social media. International Journal on Cyber Situational Awareness (IJCSA), 1 (1). ISSN 2057-2182 (Print), 2057-2182 (Online) (doi:

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While the human as a sensor concept has been utilised extensively for the detection of threats to safety and security in physical space, especially in emergency response and crime reporting, the concept is largely unexplored in the area of cyber security. Here, we evaluate the potential of utilising users as human sensors for the detection of cyber threats, specifically on social media. For this, we have conducted an online test and accompanying questionnaire-based survey, which was taken by 4,457 users. The test included eight realistic social media scenarios (four attack and four non-attack) in the form of screenshots, which the participants were asked to categorise as “likely attack” or “likely not attack”. We present the overall performance of human sensors in our experiment for each exhibit, and also apply logistic regression and Random Forest classifiers to evaluate the feasibility of predicting that performance based on different characteristics of the participants. Such prediction would be useful where accuracy of human sensors in detecting and reporting social media security threats is important. We identify features that are good predictors of a human sensor’s performance and evaluate them in both a theoretical ideal case and two more realistic cases, the latter corresponding to limited access to a user’s characteristics.

Item Type: Article
Uncontrolled Keywords: Cyber security; Semantic social engineering; Cyber attacks; Cyber crime; Social media; Social networks
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC)
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
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Last Modified: 26 Nov 2020 22:34
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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