Detecting Fake Escrow Websites using Rich Fraud Cues and Kernel Based Methods
This addresses online auction fraud by providing a domain-specific solution for detecting fake escrow websites, though it appears incremental as it builds on existing methods with tailored features.
The study tackled the problem of automatically detecting fraudulent escrow websites by evaluating features and techniques, achieving over 98% accuracy in differentiating fake from real sites using a composite kernel and support vector machines.
The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud. Despite research on related topics, fake escrow website categorization has received little attention. In this study we evaluated the effectiveness of various features and techniques for detecting fake escrow websites. Our analysis included a rich set of features extracted from web page text, image, and link information. We also proposed a composite kernel tailored to represent the properties of fake websites, including content duplication and structural attributes. Experiments were conducted to assess the proposed features, techniques, and kernels on a test bed encompassing nearly 90,000 web pages derived from 410 legitimate and fake escrow sites. The combination of an extended feature set and the composite kernel attained over 98% accuracy when differentiating fake sites from real ones, using the support vector machines algorithm. The results suggest that automated web-based information systems for detecting fake escrow sites could be feasible and may be utilized as authentication mechanisms.