JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network-Reference-Cited by-同舟云学术

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JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network

Published:2022-07 Issue: Volume:118 Page:102715
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Fang Yong,Huang Chaoyi,Zeng Minchuan,Zhao Zhiying,Huang Cheng^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference42 articles.

1. Determinacy in static analysis for jQuery;Andreasen,2014

2. Finding and preventing bugs in javascript bindings;Brown,2017

3. Malware detection based on graph attention networks for intelligent transportation systems;Catal;Electronics (Basel),2021

4. Deepwukong: statically detecting software vulnerabilities using deep graph neural network;Cheng;ACM Transactions on Software Engineering and Methodology (TOSEM),2021

5. Learning phrase representations using rnn encoder-decoder for statistical machine translation;Cho;arXiv preprint arXiv:1406.1078,2014

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1. A Model Based on GCN and TCN for Malicious Code Detection in Power Information System;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

2. Few Edges are Enough: Few-Shot Network Attack Detection with Graph Neural Networks;Lecture Notes in Computer Science;2024

3. A Comparative Analysis of Deep Learning Approaches for Enhancing Security in Web Applications;Lecture Notes in Networks and Systems;2024

4. A key code detection model based on semantic convolutional memory fusion network;Fourth International Conference on Signal Processing and Computer Science (SPCS 2023);2023-12-21

5. An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors;Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis;2023-07-12

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