Old Habits Die Hard: Fingerprinting Websites on the Cloud

摘要

To detect malicious websites on the cloud where a variety of network traffic mixed together, precise detection method is needed. Such method ought to classify websites over composite network traffic and fit to the practical problems like unidirectional flows in ISP gateways. In this work, we investigate the website fingerprinting methods and propose a novel model to classify websites on the cloud. The proposed model can recognize websites from traffic collected with multi-tab setting and performs better than the state of the art method. Furthermore, the method keeps excellent performances with unidirectional flows and real world traffic by utilizing features only extracted from the request side.