Predicting individuals' vulnerability to social engineering in social networks

Albladi, Samar Muslah and Weir, George R. S. (2020) Predicting individuals' vulnerability to social engineering in social networks. Cybersecurity, 3 (1). 7. ISSN 2523-3246 (https://doi.org/10.1186/s42400-020-00047-5)

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Abstract

The popularity of social networking sites has attracted billions of users to engage and share their information on these networks. The vast amount of circulating data and information expose these networks to several security risks. Social engineering is one of the most common types of threat that may face social network users. Training and increasing users’ awareness of such threats is essential for maintaining continuous and safe use of social networking services. Identifying the most vulnerable users in order to target them for these training programs is desirable for increasing the effectiveness of such programs. Few studies have investigated the effect of individuals’ characteristics on predicting their vulnerability to social engineering in the context of social networks. To address this gap, the present study developed a novel model to predict user vulnerability based on several perspectives of user characteristics. The proposed model includes interactions between different social network-oriented factors such as level of involvement in the network, motivation to use the network, and competence in dealing with threats on the network. The results of this research indicate that most of the considered user characteristics are factors that influence user vulnerability either directly or indirectly. Furthermore, the present study provides evidence that individuals’ characteristics can identify vulnerable users so that these risks can be considered when designing training and awareness programs.

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)