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Social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectraldoi:10.1145/1866307.1866418 dblp:conf/ccs/YingWB10 fatcat:3a5xcwikirf7tiolsnzak5brs4