JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network
Web development technology has experienced significant progress. The creation of JavaScript has highly enriched the interactive ability of the client. However, the attacker uses the dynamic characteristics of the JavaScript language to embed malicious code into web pages to achieve the purpose of sm...
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| Vydané v: | Computers & security Ročník 118; s. 102715 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier Ltd
01.07.2022
Elsevier Sequoia S.A |
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| ISSN: | 0167-4048, 1872-6208 |
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| Abstract | Web development technology has experienced significant progress. The creation of JavaScript has highly enriched the interactive ability of the client. However, the attacker uses the dynamic characteristics of the JavaScript language to embed malicious code into web pages to achieve the purpose of smuggling, redirection, and so on. Traditional methods based on static feature detection are therefore difficult to detect malicious code after confusion, and the method based on dynamic analysis is inefficient. To meet these challenges, this paper proposes a static detection model JStrong based on graph neural network. The model first generates an abstract syntax tree from the JavaScript source code, and then adds data flow and control flow information into the program dependency graph. In addition, we embed the nodes and edges of the graph into the feature vector and fully learn the features of the whole graph through the graph neural network. We take advantage of a real-world dataset collected from the top website and GitHub to evaluate JStrong and compare it to the state-of-the-art method. Experimental results show that JStrong achieves near-perfect classification performance and is superior to the state-of-the-art method. |
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| AbstractList | Web development technology has experienced significant progress. The creation of JavaScript has highly enriched the interactive ability of the client. However, the attacker uses the dynamic characteristics of the JavaScript language to embed malicious code into web pages to achieve the purpose of smuggling, redirection, and so on. Traditional methods based on static feature detection are therefore difficult to detect malicious code after confusion, and the method based on dynamic analysis is inefficient. To meet these challenges, this paper proposes a static detection model JStrong based on graph neural network. The model first generates an abstract syntax tree from the JavaScript source code, and then adds data flow and control flow information into the program dependency graph. In addition, we embed the nodes and edges of the graph into the feature vector and fully learn the features of the whole graph through the graph neural network. We take advantage of a real-world dataset collected from the top website and GitHub to evaluate JStrong and compare it to the state-of-the-art method. Experimental results show that JStrong achieves near-perfect classification performance and is superior to the state-of-the-art method. |
| ArticleNumber | 102715 |
| Author | Zeng, Minchuan Zhao, Zhiying Huang, Chaoyi Huang, Cheng Fang, Yong |
| Author_xml | – sequence: 1 givenname: Yong surname: Fang fullname: Fang, Yong – sequence: 2 givenname: Chaoyi surname: Huang fullname: Huang, Chaoyi – sequence: 3 givenname: Minchuan surname: Zeng fullname: Zeng, Minchuan – sequence: 4 givenname: Zhiying surname: Zhao fullname: Zhao, Zhiying – sequence: 5 givenname: Cheng orcidid: 0000-0002-5871-946X surname: Huang fullname: Huang, Cheng email: opcodesec@gmail.com |
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| Cites_doi | 10.1016/j.cose.2021.102218 10.1109/TDSC.2018.2845851 10.1145/3436877 10.1145/24039.24041 10.1109/TIFS.2020.3044773 10.1016/j.aiopen.2021.01.001 10.1016/j.cose.2020.101764 10.1016/j.procs.2016.07.291 |
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| Keywords | Malicious JavaScript Scripts detection Program dependency graph Code representation Graph neural network |
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| Title | JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network |
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