The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
Proceedings of the 16th international conference on World Wide Web
Mining (Social) Network Graphs to Detect Random Link Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Detecting fraudulent personalities in networks of online auctioneers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Isolating and analyzing fraud activities in a large cellular network via voice call graph analysis
Proceedings of the 10th international conference on Mobile systems, applications, and services
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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 spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published.