Online Social Networks (OSNs) have seen an enormous boost in popularity in recent years. Along with this popularity has come tribulations such as privacy concerns, spam, phishing and malware. Many recent works have focused on automatically detecting these unwanted behaviors in OSNs so that they may be removed. These works have developed state-of-the-art detection schemes that use machine learning techniques to automatically classify OSN accounts as spam or non-spam. In this work, these detection schemes are recreated and tested on new data. Through this analysis, it is clear that spammers are beginning to evade even these detectors. The evasion tactics used by spammers are identified and analyzed. Then a new detection scheme is built upon the previous ones that is robust against these evasion tactics. Next, the difficulty of evasion of the existing detectors and the new detector are formalized and compared. This work builds a foundation for future researchers to build on so that those who would like to protect innocent internet users from spam and malicious content can overcome the advances of those that would prey on these users for a meager dollar.
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