2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) 2019
DOI: 10.1109/sibircon48586.2019.8958026
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De-Anonymization of the Author of the Source Code Using Machine Learning Algorithms

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Cited by 3 publications
(4 citation statements)
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“…The process of such identification consists of determining the author's style, identifying the programmer's individual habits, professional techniques, and approaches to writing program code. The solutions to this problem can be used as computer forensics tools [1][2][3][4].…”
Section: Introductionmentioning
confidence: 99%
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“…The process of such identification consists of determining the author's style, identifying the programmer's individual habits, professional techniques, and approaches to writing program code. The solutions to this problem can be used as computer forensics tools [1][2][3][4].…”
Section: Introductionmentioning
confidence: 99%
“…First experiments with default settings used in [1] for the Adadelta optimizer (in the PyTorch framework) do not allow achieving high accuracy in complex cases. The following experimentally founded parameters allow identifying the source code author equally well in all cases (up to 15% increase for complex cases):…”
mentioning
confidence: 99%
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“…The relevance of research on this topic is associated with the auxiliary function for solving the problems of text mining [1][2][3][4][5][6][7], particularly its attribution [8,9]. The sentiment of the text, as well as the gender and age of the author, are the most informative features used in attribution.…”
Section: Introductionmentioning
confidence: 99%