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Evaluating the reliability of users as human sensors of social media security threats

Evaluating the reliability of users as human sensors of social media security threats

Heartfield, Ryan and Loukas, George (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).

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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 / Department / Research Group: Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Related URLs:
Last Modified: 01 Dec 2016 16:49
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/15019

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