Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning

Abstract

The frequent variations of XSS (cross-site scripting) payloads make static and dynamic analysis difficult to detect effectively. In this paper, we proposed a fusion verification method that combines traffic detection with XSS payload detection, using machine learning to detect XSS attacks. In addition, we also proposed seven new payload features to improve detection efficiency. In order to verify the effectiveness of our method, we simulated and tested 20 public CVE (Common Vulnerabilities and Exposures) XSS attacks. The experimental results show that our proposed method has better accuracy than the single traffic detection model. Among them, the recall rate increased by an average of 48%, the F1 score increased by an average of 27.94%, the accuracy rate increased by 9.29%, and the accuracy rate increased by 3.81%. Moreover, the seven new features proposed in this paper account for 34.12% of the total contribution rate of the classifier.