A conceptual model to predict social engineering victims

Muslah Albladi, Samar and Weir, George R.S.; (2019) A conceptual model to predict social engineering victims. In: 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3). Institute of Electrical and Electronics Engineers Inc., GBR. ISBN 9781538670019 (https://doi.org/10.1109/ICGS3.2019.8688352)

[thumbnail of Albladi-Weir-ICGS3-2019-A-conceptual-model-to-predict-social-engineering]
Text. Filename: Albladi_Weir_ICGS3_2019_A_conceptual_model_to_predict_social_engineering.pdf
Accepted Author Manuscript

Download (319kB)| Preview


Social engineering (SE) attacks are a serious threat to online users and might subject people to different kinds of harm. Despite increased concern with this risk, there has been little research activity focused upon social engineering in the potentially rich hunting ground of social networks. The number of victims of social engineering attacks will be decreased if the users' detection ability has improved. Yet, this improvement of the user's detection behaviour can't be occurred without investigating the users' weakness points. The present study develops a conceptual model to test the factors that influence social networks users' judgment of social engineering-based attacks in order to identify the weakest points of users' detection behaviour which also help to predict vulnerable individuals.


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