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DEOBFUSCATING JAVASCRIPT CODE USING CHARACTER-BASED TOKENIZATION.

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Titel: DEOBFUSCATING JAVASCRIPT CODE USING CHARACTER-BASED TOKENIZATION.
Autoren: SÎRBU, ALEXANDRU-GABRIEL
Quelle: Studia Universitatis Babeş-Bolyai, Informatica; Jul-Dec2023, Vol. 68 Issue 2, p5-21, 17p
Schlagwörter: MACHINE learning, JAVASCRIPT programming language, RECURRENT neural networks, DEEP learning, SYNTAX (Grammar)
Abstract: 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 understand 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%. [ABSTRACT FROM AUTHOR]
Copyright of Studia Universitatis Babeş-Bolyai, Informatica is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: DEOBFUSCATING JAVASCRIPT CODE USING CHARACTER-BASED TOKENIZATION.
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  Data: Studia Universitatis Babeş-Bolyai, Informatica; Jul-Dec2023, Vol. 68 Issue 2, p5-21, 17p
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  Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22JAVASCRIPT+programming+language%22">JAVASCRIPT programming language</searchLink><br /><searchLink fieldCode="DE" term="%22RECURRENT+neural+networks%22">RECURRENT neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22SYNTAX+%28Grammar%29%22">SYNTAX (Grammar)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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 understand 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%. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Studia Universitatis Babeş-Bolyai, Informatica is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.24193/subbi.2023.2.01
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 5
    Subjects:
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: JAVASCRIPT programming language
        Type: general
      – SubjectFull: RECURRENT neural networks
        Type: general
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: SYNTAX (Grammar)
        Type: general
    Titles:
      – TitleFull: DEOBFUSCATING JAVASCRIPT CODE USING CHARACTER-BASED TOKENIZATION.
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            – D: 01
              M: 07
              Text: Jul-Dec2023
              Type: published
              Y: 2023
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              Value: 68
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            – TitleFull: Studia Universitatis Babeş-Bolyai, Informatica
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