JACLNet:Application of adaptive code length network in JavaScript malicious code detection

Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, compo...

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Veröffentlicht in:	PloS one Jg. 17; H. 12; S. e0277891
Hauptverfasser:	Zhang, Zhining, Wan, Liang, Chu, Kun, Li, Shusheng, Wei, Haodong, Tang, Lu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 14.12.2022 Public Library of Science (PLoS)
Schlagworte:	Algorithms Biology and Life Sciences Computer and Information Sciences Computer Security Data security Datasets Deep learning Feature extraction Java Java (Computer program language) JavaScript Machine learning Malware Management Neural networks Physical Sciences Prevention Recombination Research and Analysis Methods Social Sciences Spyware Syntax Trees China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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