.png){kind=icon}
Return
MFXSS: An effective XSS vulnerability detection method in JavaScript based on multi-feature model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cose.2022.103015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The widespread use of web applications has also made them more vulnerable to hackers, resulting in the leakage of large amounts of application and personal privacy data. Cross-site scripting (XSS) attacks are one of the most significant threats to web applications. Attackers can inject scripts to control the victim's browser to send data or execute commands, leading to the theft of privacy or the hijacking of login to-kens. Therefore, we proposed a multi-feature fusion-based neural network vulnerability detection model for detecting XSS vulnerabilities in the JavaScript source code of websites (We termed our implementa-tion of this approach MFXSS). We combine abstract syntax tree (AST) and code control flow graph (CFG) to convert the generalized sample data into graph structure and code string structure. Then, through the graph convolutional neural network, weighted aggregation, and the bidirectional recurrent neural net-work, the logical call features and the context execution relationship features of the source code are extracted and fused respectively. Finally, the fused feature vectors are used to detect and predict XSS vulnerabilities in JavaScript. In the experiment, we designed multiple control experiments to verify that the model construction is optimal, and the accuracy rates in the standard and variant datasets are 0.997 and 0.986. Moreover, in comparing similar detection schemes, MFXSS also performs better. Applying the model to an actual web environment, we successfully detected the presence of XSS vulnerabilities in websites.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Cross -site scripting
Multi -feature fusion
Graph convolutional network
Weighted aggregation
Vulnerability detection
Journal
C
IF:
5.4
Papers:
4.6K
Citations:
1.4W
Organization
Cited Papers
ShareSaveRe-Cloning the N27 Dopamine Cell Line to Improve a Cell Culture Model of Parkinson's Disease.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveLate Bronze Age climate change and the destruction of the Mycenaean Palace of Nestor at PylosPLOS ONEOAAI
PLOS ONEIF0
OAAIShareSaveShareSaveFramewise phoneme classification with bidirectional LSTM and other neural network architecturesNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSaveUnderstanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanismSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
ShareSaveGraphXSS: An efficient XSS payload detection approach based on graph convolutional networkCOMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
ShareSaveShareSave
View more