Abstract and subjects
Online social networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these online social networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in online social networks. Our study is based on a dataset collected from a real-world location-based online social network, which includes not only the social graph formed by its users but also the users' activity events. We analyze the social structure and user activity patterns of this network, and confirm that it is a typical online social network, suggesting that conclusions drawn from this specific network can be translated to other online social networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, social structure, and activity patterns on malware propagation in online social networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in online social networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of online social network malware and also shed light on how to defend against them effectively.