Towards automatic real time identification of malicious posts on Facebook

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Abstract
Online Social Networks (OSNs) witness a rise in user activity whenever a news-making event takes place. Cyber criminals exploit this spur in user-engagement levels to spread malicious content that compromises system reputation, causes financial losses and degrades user experience. In this paper, we characterized a dataset of 4.4 million public posts generated on Facebook during 17 news-making events (natural calamities, terror attacks, etc.) and identified 11,217 malicious posts containing URLs. We found that most of the malicious content which is currently evading Facebook's detection techniques originated from third party and web applications, while more than half of all legitimate content originated from mobile applications. We also observed greater participation of Facebook pages in generating malicious content as compared to legitimate content. We proposed an extensive feature set based on entity profile, textual content, metadata, and URL features to automatically identify malicious content on Facebook in real time. This feature set was used to train multiple machine learning models and achieved an accuracy of 86.9%. We performed experiments to show that past techniques for spam campaign detection identified less than half the number of malicious posts as compared to our model. This model was used to create a REST API and a browser plug-in to identify malicious Facebook posts in real time.