Online Social Networks (OSNs) witness a rise in user activity whenever an event takes place. Malicious entities exploit this spur in user-engagement levels to spread malicious content that compromises system reputation and degrades user experience. It also generates revenue from advertisements, clicks, etc. for the malicious entities. Facebook, the world's biggest social network, is no exception and has recently been reported to face much abuse through scams and other type of malicious content, especially during news making events. Recent studies have reported that spammers earn $200 million just by posting malicious links on Facebook. In this paper, we characterize malicious content posted on Facebook during 17 events, and discover that existing efforts to counter malicious content by Facebook are not able to stop all malicious content from entering the social graph. Our findings revealed that malicious entities tend to post content through web and third party applications while legitimate entities prefer mobile platforms to post content. In addition, we discovered a substantial amount of malicious content generated by Facebook pages. Through our observations, we propose an extensive feature set based on entity profile, textual content, metadata, and URL features to identify malicious content on Facebook in real time and at zero-hour. This feature set was used to train multiple machine learning models and achieved an accuracy of 86.9 . The intent is to catch malicious content that is currently evading Facebook's detection techniques. Our machine learning model was able to detect more than double the number of malicious posts as compared to existing malicious content detection techniques. Finally, we built a real world solution in the form of a REST based API and a browser plug-in to identify malicious Facebook posts in real time.
Facebook has its own immune system @cite_3 to safeguard its users from unwanted malicious content. Researchers at Facebook built and deployed a coherent, scalable, and extensible real time system to protect their users and the social graph. This system performs real time checks and classifications on every read and write action. Designers of this complex system used an exhaustive set of components and techniques to differentiate between legitimate actions and spam. These components were standard classifiers like Random Forest, Support Vector Machines, Logistic Regression, a feature extraction language, dynamic model loading, a policy engine, and feature loops. Interestingly, despite this complex immune system deployed by Facebook, unwanted spam, phishing, and other malicious content continues to exist and thrive on Facebook. Although the immune system deployed by Facebook utilizes a variety of techniques to safeguard its users, authors did not present an evaluation of the system to suggest how accurately and efficiently the system is able to capture malicious content.
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Popular Internet sites are under attack all the time from phishers, fraudsters, and spammers. They aim to steal user information and expose users to unwanted spam. The attackers have vast resources at their disposal. They are well-funded, with full-time skilled labor, control over compromised and infected accounts, and access to global botnets. Protecting our users is a challenging adversarial learning problem with extreme scale and load requirements. Over the past several years we have built and deployed a coherent, scalable, and extensible realtime system to protect our users and the social graph. This Immune System performs realtime checks and classifications on every read and write action. As of March 2011, this is 25B checks per day, reaching 650K per second at peak. The system also generates signals for use as feedback in classifiers and other components. We believe this system has contributed to making Facebook the safest place on the Internet for people and their information. This paper outlines the design of the Facebook Immune System, the challenges we have faced and overcome, and the challenges we continue to face.