Machine Learning Based Cross-Site Scripting Detection in Online Social Network

Nowadays online social network (OSN) is one of the most popular internet services in the world. It allows us to communicate with others and share knowledge. However, from the security's point of view, OSN is becoming the favorite target for the attackers, and is under a lot of threats such as cross-site scripting (XSS) attacks. In this paper, we present a novel approach using machine learning to do XSS detection in OSN. Firstly, we leverage a new method to capture identified features from web pages and then establish classification models which can be used in XSS detection. Secondly, we propose a novel method to simulate XSS worm spreading and build our webpage database. Finally, we set up experiments to verify the classification models using our test database. Our experiment results demonstrate that our approach is an effective countermeasure to detect the XSS attack.