Research on XSS adversarial attack model based on reinforcement learning

Ming Zhu; yufeng he; Qinglan Luo; Xia Shang

doi:10.1117/12.3010555

20 October 2023 Research on XSS adversarial attack model based on reinforcement learning

Ming Zhu, yufeng he, Qinglan Luo, Xia Shang

Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 128141E (2023) https://doi.org/10.1117/12.3010555
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China

Abstract

Cross-site scripting (XSS) is an important issue in the field of network security, and there have been many studies on XSS detection models. However, the emergence of XSS adversarial attack samples has affected the detection accuracy of these models. Therefore, this paper proposes a reinforcement learning-based XSS adversarial attack model, which aims to generate XSS adversarial attack samples and consists of a detection module and an adversarial attack module. In the detection stage, the detection module cleans the original XSS script data and vectorizes it using Word2Vec. Then, the processed data is input into the XSS detection model of the detection module. The detection module uses an ensemble learning method to construct an XSS detection model by combining LSTM, MLP, and SVM to form a higher accuracy model. Finally, the detection module obtains the classification result of the original XSS data as a preparation for the next escape stage. In the escape stage, the adversarial attack module is designed using the reinforcement learning algorithm TD3, and uses an adversarial generation module to generate legitimate adversarial samples that can bypass the XSS detection model. Experimental results show that the XSS adversarial attack samples generated based on TD3 have an evasion rate nearly 6% higher than those based on Soft-Q learning. This model provides a new idea and method for improving the accuracy of XSS detection models and providing more valuable XSS attack data samples.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Zhu, yufeng he, Qinglan Luo, and Xia Shang "Research on XSS adversarial attack model based on reinforcement learning", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 128141E (20 October 2023); https://doi.org/10.1117/12.3010555

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Adversarial training

Statistical modeling

Data modeling

Detection and tracking algorithms

Education and training

Sampling rates

Deep learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years