Application of machine learning to identify Counterfeit Website
Kuan-Ting Wu, ShingHua Chou, Shyh-Wei Chen, Ching-Tsorng Tsai, Shyan‐Ming Yuan · 2018
Recent years the prevalence of fraudulent websites has become more severe than before. Fraudulent ecommerce websites that sell counterfeit goods not only cost financial damage to consumers but also have a great impact on Internet industry. Nowadays, there is not an effective way to confront these websites. In this paper, we look forward to achieving three goals: find the characteristics of counterfeit websites, train models for classifying ecommerce websites and provide a service to help consumers distinguish counterfeit websites from legitimate ones.