  Existing fake website detection systems are unable to effectively detect fake
websites. In this study, we advocate the development of fake website detection
systems that employ classification methods grounded in statistical learning
theory (SLT). Experimental results reveal that a prototype system developed
using SLT-based methods outperforms seven existing fake website detection
systems on a test bed encompassing 900 real and fake websites.
