Abstract
Facebook is the world’s largest Online Social Network, having more than one billion users. Like most social networks, Facebook is home to various categories of hostile entities who abuse the platform by posting malicious content. In this chapter, we identify and characterize Facebook pages that engage in spreading URLs pointing to malicious domains. We revisit the scope and definition of what is deemed as “malicious” in the modern day Internet, and identify 627 pages publishing untrustworthy information, misleading content, adult and child unsafe content, scams, etc. We perform in-depth characterization of pages through spatial and temporal analysis. Upon analyzing these pages, our findings reveal dominant presence of politically polarized entities engaging in spreading content from untrustworthy web domains. Studying the temporal posting activity of pages reveals that malicious pages are 1.4 times more active daily than benign pages. We further identify collusive behavior within a set of malicious pages spreading adult and pornographic content. Finally, we attempt to automate the process of detecting malicious Facebook pages by extensively experimenting with multiple supervised learning algorithms and multiple feature sets. Artificial neural networks trained on a fixed sized bag-of-words perform the best and achieve a maximum ROC area under curve value of 0.931.
This chapter is an extended version of the paper titled “Hiding in Plain Sight: Characterizing and Detecting Malicious Facebook Pages” previously accepted at ASONAM 2016.
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Exact description for each of these attributes can be found at https://developers.facebook.com/docs/graph-api/reference/page/.
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Acknowledgements
We would like to thank all the members of Precog Research Group and Cybersecurity Education and Research Centre (CERC) at IIIT Delhi for their constant support and feedback for this work.
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Dewan, P., Bagroy, S., Kumaraguru, P. (2018). Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook. In: Kaya, M., Kawash, J., Khoury, S., Day, MY. (eds) Social Network Based Big Data Analysis and Applications. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-78196-9_2
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