Evaluating the reliability of users as human sensors of social media security threats
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Heartfield, Ryan and Loukas, George 
ORCID: https://orcid.org/0000-0003-3559-5182
(2016)
Evaluating the reliability of users as human sensors of social media security threats.
In: International Conference on Social Media, Wearable and Web Analytics (Social Media 2016) (Book of abstracts).
The Centre for Multidisciplinary Research, Innovation and Collaboration (C-MRiC.ORG).
Abstract
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 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: | Conference Proceedings |
|---|---|
| Title of Proceedings: | International Conference on Social Media, Wearable and Web Analytics (Social Media 2016) (Book of abstracts) |
| Additional Information: | International Conference on Social Media, Wearable and Web Analytics (Social Media 2016), June 13-14, 2016, London, UK |
| Uncontrolled Keywords: | Predicting attack susceptibility, phishing, semantic social engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) |
| Related URLs: | |
| Last Modified: | 26 Nov 2020 22:34 |
| URI: | http://gala.gre.ac.uk/id/eprint/15019 |
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