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The ability to automatically detect fraudulent escrow websites is importantin order to alleviate online auction fraud. Despite research on related topics,fake escrow website categorization has received little attention. In this studywe evaluated the effectiveness of various features and techniques for detectingfake escrow websites. Our analysis included a rich set of features extractedfrom web page text, image, and link information. We also proposed a compositekernel tailored to represent the properties of fake websites, including contentduplication and structural attributes. Experiments were conducted to assess theproposed features, techniques, and kernels on a test bed encompassing nearly90,000 web pages derived from 410 legitimate and fake escrow sites. Thecombination of an extended feature set and the composite kernel attained over98% accuracy when differentiating fake sites from real ones, using the supportvector machines algorithm. The results suggest that automated web-basedinformation systems for detecting fake escrow sites could be feasible and maybe utilized as authentication mechanisms.

Detecting Fake Escrow Websites using Rich Fraud Cues and Kernel Based Methods