Improved N-gram approach for cross-site scripting detection in Online Social Network

Nowadays Online Social Networks (OSNs) have become a popular web service in the world. With the development of mobile networks, OSNs provide users with online communication platform. However, the OSNs' openness leads to so much exposure that it brings many new security threats, such as cross-site scripting (XSS) attacks. In this paper, we present a novel approach using classifiers and the improved n-gram model to do the XSS detection in OSN. Firstly, we identify a group of features from webpages and use them to generate classifiers for XSS detection. Secondly, we present an improved n-gram model (a model derived from n-gram model) built from the features to classify webpages. Thirdly, we propose an approach based on the combination of classifiers and the improved n-gram model to detect XSS in OSN. Finally, a method is proposed to simulate XSS worm spread in OSN to get more accurate experiment data. Our experiment results demonstrate that our approach is effective in OSN's XSS detection.