Web Scams Detection System

Web-based scams rely on scam websites to provide fraudulent business or fake services to steal money and sensitive information from unsuspecting victims. Despite many researchers’ efforts to develop anti-scam detection techniques, their main focus has been on understanding, detecting, and analyzing scam sites. State-of-the-art anti-scam research still faces several challenges, such as acquiring a properly labeled scam dataset, especially when there is no blacklist, central repository, or previous large-scale analysis. The researchers have created labeled datasets in different ways, such as manually collecting and labeling the dataset or using a semi-automatic crawler followed by manual inspection. However, this process requires previous knowledge and understanding of the scam and much manual work. In this paper, we propose a data-driven model to create a labeled training dataset for web-based scams that have a web presence. Given a small scam sample, our model formulates scam-related search queries and uses them on multiple search engines to search for, and collect, potential scam pages. After collecting a sufficiently large corpus of web pages, our model semi-automatically clusters the search results and creates a labeled training dataset with minimal human interaction. We have validated our model using two different scam types that we have studied in our previous work. We tested our classifiers against the databases of web pages we collected during our previous analysis of the scams and successfully detected more than 87% of the scam pages while maintaining a false positive value as low as 0.23%.