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Fake online pharmacies have become increasingly pervasive, constituting over90% of online pharmacy websites. There is a need for fake website detectiontechniques capable of identifying fake online pharmacy websites with a highdegree of accuracy. In this study, we compared several well-known link-baseddetection techniques on a large-scale test bed with the hyperlink graphencompassing over 80 million links between 15.5 million web pages, including1.2 million known legitimate and fake pharmacy pages. We found that the QoC andQoL class propagation algorithms achieved an accuracy of over 90% on ourdataset. The results revealed that algorithms that incorporate dual classpropagation as well as inlink and outlink information, on page-level orsite-level graphs, are better suited for detecting fake pharmacy websites. Inaddition, site-level analysis yielded significantly better results thanpage-level analysis for most algorithms evaluated.

Evaluating Link-Based Techniques for Detecting Fake Pharmacy Websites