Analysis of XSS attack based on ensemble learning

Ruiheng Liu; Boyao You; Changqing Jin; Chengying Zhu; Wenlong Wang; Haolin Jin

doi:10.1117/12.2639954

19 October 2022 Analysis of XSS attack based on ensemble learning

Ruiheng Liu, Boyao You, Changqing Jin, Chengying Zhu, Wenlong Wang, Haolin Jin

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945D (2022) https://doi.org/10.1117/12.2639954
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

In this paper, we collected real XSS attack logs from web servers for the problem of XSS attack defense in real environment. Relevant features were selected and a low memory consumption XSS matching filtering model was constructed using the Random Forest algorithm. By comparing with traditional machine learning and other integrated algorithms, it is found that Random Forest is faster to train and consumes less memory during training while ensuring accuracy. The experimental results show that the model has better classification results and is more suitable for deployment on browser clients.

Citation Download Citation

Ruiheng Liu, Boyao You, Changqing Jin, Chengying Zhu, Wenlong Wang, and Haolin Jin "Analysis of XSS attack based on ensemble learning", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945D (19 October 2022); https://doi.org/10.1117/12.2639954

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