Spam ain't as diverse as it seems: throttling OSN spam with templates underneath

Abstract

In online social networks (OSNs), spam originating from friends and acquaintances not only reduces the joy of Internet surfing but also causes damage to less security-savvy users. Prior countermeasures combat OSN spam from different angles. Due to the diversity of spam, there is hardly any existing method that can independently detect the majority or most of OSN spam. In this paper, we empirically analyze the textual pattern of a large collection of OSN spam. An inspiring finding is that the majority (63.0%) of the collected spam is generated with underlying templates. We therefore propose extracting templates of spam detected by existing methods and then matching messages against the templates toward accurate and fast spam detection. We implement this insight through Tangram, an OSN spam filtering system that performs online inspection on the stream of user-generated messages. Tangram automatically divides OSN spam into segments and uses the segments to construct templates to filter future spam. Experimental results show that Tangram is highly accurate and can rapidly generate templates to throttle newly emerged campaigns. Specifically, Tangram detects the most prevalent template-based spam with 95.7% true positive rate, whereas the existing template generation approach detects only 32.3%. The integration of Tangram and its auxiliary spam filter achieves an overall accuracy of 85.4% true positive rate and 0.33% false positive rate.