Efficient and scalable socware detection in online social networks

Abstract

Online social networks (OSNs) have become the new vec-tor for cybercrime, and hackers are finding new ways to propagate spam and malware on these platforms, which we refer to as socware. As we show here, socware cannot be identified with existing security mechanisms (e.g., URL blacklists), because it exploits different weaknesses and of-ten has different intentions. In this paper, we present MyPageKeeper, a Facebook ap-plication that we have developed to protect Facebook users from socware. Here, we present results from the perspective of over 12K users who have installed MyPageKeeper and their roughly 2.4 million friends. Our work makes three main contributions. First, to enable protection of users at scale, we design an efficient socware detection method which takes advantage of the social context of posts. We find that our classifier is both accurate (97 % of posts flagged by it are indeed socware and it incorrectly flags only 0.005 % of benign posts) and efficient (it requires 46 ms on average to classify a post). Second, we show that socware significantly differs from traditional email spam or web-based malware. For example, website blacklists identify only 3 % of the posts flagged by MyPageKeeper, while 26 % of flagged posts point to malicious apps and pages hosted on Facebook (which no current antivirus or blacklist is designed to detect). Third, we quantify the prevalence of socware by analyzing roughly 40 million posts over four months; 49 % of our users were exposed to at least one socware post in this period. Finally, we identify a new type of parasitic behavior, which we refer to as “Like-as-a-Service”, whose goal is to artificially boost the number of “Likes ” of a Facebook page. 1