AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: ALEXANDRU-GABRIEL SÎRBU.
DOI: 10.24193/subbi.2023.2.01
Published Online: 2023-12-22
pp. 5-21
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The JavaScript code deployed goes through the process of minification, in which variables are renamed using single-character names and spaces are removed in order for the files to have a smaller size, thus loading faster. Because of this, the code becomes unintelligible, making it harder to be analyzed manually. Since JavaScript experts can under- stand it, machine learning approaches to deobfuscate the minified file are possible. Thus, we propose a technique that finds a fitting name for each obfuscated variable, which is both intuitive and meaningful based on the usage of that variable, based on a Sequence-to-Sequence model, which generates the name character by character to cover all the possible variable names. The proposed approach achieves an average exact name generation accuracy of 70.53%, outperforming the state-of-the-art by 12%.
Received by the editors: 31 July 2023.
2010 Mathematics Subject Classification 68T05, 68T50.
1998 CR Categories and Descriptors. I.2.6 [Learning]: Subtopic – Connectionism and neural nets; I.2.7 [Natural Language Processing]: Subtopic – Language generation.
Keywords and phrases: JavaScript deobfuscation, variable name prediction, Deep Learning, Recurrent Neural Network, Abstract Syntax Tree.
