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Combating web spam with trustrank
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Search engines are currently facing a problem of websites that distribute malware. In this paper we present a novel efficient algorithm that learns to detect such kind of spam. We have used a bipartite graph with two types of nodes, each representing a layer in the graph: web-sites and file hostings (FH), connected with edges representing the fact that a file can be downloaded from the hosting via a link on the web-site. The performance of this spam detection method was verified using two set of ground truth labels: manual assessments of antivirus analysts and automatically generated assessments obtained from antivirus companies. We demonstrate that the proposed method is able to detect new types of malware even before the best known antivirus solutions are able to detect them.