As Cross-Site Scripting (XSS) remains one of the top web security risks, people keep exploring ways to detect such attacks efficiently. So far, existing solutions only focus on the payload in a web request or a response, a single stage of a web transaction. This work proposes a new approach that integrates evidences from both a web request and its response in order to better characterize XSS attacks and separate them from normal web transactions. We first collect complete payloads of XSS and normal web transactions from two databases and extract features from them using the Word2vec technique. Next, we train two Gaussian mixture models (GMM) with these features, one for XSS transaction and one for normal web transactions. These two models can generate two probability scores for a new web transaction, which indicate how similar this web transaction is to XSS and normal traffics respectively. Finally, we put together these two GMM models in classification by combining these two probabilities to further improve detection accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.