Rating Web Pages Using Page-Transition Evidence

The rating of web pages is an important metric that has wide applications, such as web search and malicious page detection. Existing solutions for web page rating rely on either subjective opinions or overall page relationships. In this paper, we present a new solution, SnowEye, to decide the trust rating of web pages with evidence obtained from browsers. The intuition of our approach is that user-activated page transition behaviors provide dynamic evidence to evaluate the rating of web pages. We present an algorithm to rate web pages based on page transitions triggered by users. We prototyped our approach in the Google Chrome browser. Our evaluation through real-world websites and simulation supports our intuition and verifies the correctness of our approach.