The physical world is rife with cues that allow us to distinguish between safe and unsafe situations. By contrast, the Internet of- fers a much more ambiguous environment; hence many users are unable to distinguish a scam from a legitimate Web page. To help address this problem, we explore how to train classifiers that can automatically identify malicious Web pages based on clues from their textual content, structural tags, page links, visual appearance, and URLs. Using a contemporary labeled data feed from a large Web mail provider, we extract such features and demonstrate how they can be used to improve classification accuracy over previous, more constrained approaches. In particular, by analyzing the full content of individualWeb pages, we more than halve the error rate obtained by a comparably trained classifier that only extracts fea- tures from URLs. By training classifiers on different sets of fea- tures, we are further able to assess the strength of clues provided by these different sources of information.
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