document: Beyond blacklists : learning to detect malicious web sites from suspicious URLs Malicious Web sites are a cornerstone of Internet criminal activities . As a result , there has been broad interest in developing systems to prevent the end user from visiting such sites . In this paper , we describe an approach to this problem based on automated URL classification , using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs . These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs . The resulting classifiers obtain 95-99 % accuracy , detecting large numbers of malicious Web sites from their URLs , with only modest false positives .
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