A semi-automated security advisory system to resist cyber-attack in social networks

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Albladi, Samar Muslah and Weir, George R.S.; (2018) A semi-automated security advisory system to resist cyber-attack in social networks. In: Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11055 . Springer-Verlag, GBR, pp. 146-156. ISBN 9783319984421 (https://doi.org/10.1007/978-3-319-98443-8_14)

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Abstract

Social networking sites often witness various types of social engineering (SE) attacks. Yet, limited research has addressed the most severe types of social engineering in social networks (SNs). The present study investigates the extent to which people respond differently to different types of attack in a social network context and how we can segment users based on their vulnerability. In turn, this leads to the prospect of a personalised security advisory system. 316 participants have completed an online-questionnaire that includes a scenario-based experiment. The study result reveals that people respond to cyber-attacks differently based on their demographics. Furthermore, people’s competence, social network experience, and their limited connections with strangers in social networks can decrease their likelihood of falling victim to some types of attacks more than others.

ORCID iDs

Albladi, Samar Muslah ORCID logoORCID: https://orcid.org/0000-0001-9246-9540 and Weir, George R.S. ORCID logoORCID: https://orcid.org/0000-0002-6264-4480;
CORE (COnnecting REpositories)