Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable -- they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.
@cite_13 studied new Twitter spammers that act in a stealthy way to avoid detection. In their system, they use advanced features such as the topology of the network that surrounds the spammer. They do not try to distinguish compromised from spam accounts.
@cite_13
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Due to the significance and indispensability of detecting and suspending Twitter spammers, many researchers along with the engineers in Twitter Corporation have devoted themselves to keeping Twitter as spam-free online communities. Meanwhile, Twitter spammers are also evolving to evade existing detection techniques. In this paper, we make an empirical analysis of the evasion tactics utilized by Twitter spammers, and then design several new and robust features to detect Twitter spammers. Finally, we formalize the robustness of 24 detection features that are commonly utilized in the literature as well as our proposed ones. Through our experiments, we show that our new designed features are effective to detect Twitter spammers, achieving a much higher detection rate than three state-of-the-art approaches [35,32,34] while keeping an even lower false positive rate.