De-Anonymization of the Author of the Source Code Using Machine Learning Algorithms

Kurtukova, Anna; Romanov, Aleksandr; Fedotova, Anastasia

doi:10.1109/sibircon48586.2019.8958026

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%

“…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%

“…In this case, out 20% of the vector elements are zeroed and the model does not face the problem of overfitting. As in the previous work [1], Softmax [29], a generalization of the logistic function for the multidimensional case, was used as a function of the HNN output layer. Cross-entropy was used as a loss function, respectively.…”

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confidence: 99%

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Source Code Authorship Identification Using Deep Neural Networks

2020

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Many open-source projects are developed by the community and have a common basis. The more source code is open, the more the project is open to contributors. The possibility of accidental or deliberate use of someone else’s source code as a closed functionality in another project (even a commercial) is not excluded. This situation could create copyright disputes. Adding a plagiarism check to the project lifecycle during software engineering solves this problem. However, not all code samples for comparing can be found in the public domain. In this case, the methods of identifying the source code author can be useful. Therefore, identifying the source code author is an important problem in software engineering, and it is also a research area in symmetry. This article discusses the problem of identifying the source code author and modern methods of solving this problem. Based on the experience of researchers in the field of natural language processing (NLP), the authors propose their technique based on a hybrid neural network and demonstrate its results both for simple cases of determining the authorship of the code and for those complicated by obfuscation and using of coding standards. The results show that the author’s technique successfully solves the essential problems of analogs and can be effective even in cases where there are no obvious signs indicating authorship. The average accuracy obtained for all programming languages was 95% in the simple case and exceeded 80% in the complicated ones.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Source Code Authorship Identification Using Deep Neural Networks

2020

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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%

Determining the Age of the Author of the Text Based on Deep Neural Network Models

et al. 2020

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This paper is devoted to solving the problem of determining the age of the author of the text based on models of deep neural networks. The article presents an analysis of methods for determining the age of the author of a text and approaches to determining the age of a user by a photo. This could be a solution to the problem of inaccurate data for training by filtering out incorrect user-specified age data. A detailed description of the author’s technique based on deep neural network models and the interpretation of the results is also presented. The study found that the proposed technique achieved 82% accuracy in determining the age of the author from Russian-language text, which makes it competitive in comparison with approaches for other languages.

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Authorship Identification of a Russian-Language Text Using Support Vector Machine and Deep Neural Networks

et al. 2020

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The article explores approaches to determining the author of a natural language text and the advantages and disadvantages of these approaches. The importance of the considered problem is due to the active digitalization of society and reassignment of most parts of the life activities online. Text authorship methods are particularly useful for information security and forensics. For example, such methods can be used to identify authors of suicide notes, and other texts are subjected to forensic examinations. Another area of application is plagiarism detection. Plagiarism detection is a relevant issue both for the field of intellectual property protection in the digital space and for the educational process. The article describes identifying the author of the Russian-language text using support vector machine (SVM) and deep neural network architectures (long short-term memory (LSTM), convolutional neural networks (CNN) with attention, Transformer). The results show that all the considered algorithms are suitable for solving the authorship identification problem, but SVM shows the best accuracy. The average accuracy of SVM reaches 96%. This is due to thoroughly chosen parameters and feature space, which includes statistical and semantic features (including those extracted as a result of an aspect analysis). Deep neural networks are inferior to SVM in accuracy and reach only 93%. The study also includes an evaluation of the impact of attacks on the method on models’ accuracy. Experiments show that the SVM-based methods are unstable to deliberate text anonymization. In comparison, the loss in accuracy of deep neural networks does not exceed 20%. Transformer architecture is the most effective for anonymized texts and allows 81% accuracy to be achieved.

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De-Anonymization of the Author of the Source Code Using Machine Learning Algorithms

Cited by 3 publications

References 12 publications

Source Code Authorship Identification Using Deep Neural Networks

Source Code Authorship Identification Using Deep Neural Networks

Determining the Age of the Author of the Text Based on Deep Neural Network Models

Authorship Identification of a Russian-Language Text Using Support Vector Machine and Deep Neural Networks

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