IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v159y2020ics004016252031012x.html
   My bibliography  Save this article

Programmers' de-anonymization using a hybrid approach of abstract syntax tree and deep learning

Author

Listed:
  • Ullah, Farhan
  • Jabbar, Sohail
  • Al-Turjman, Fadi

Abstract

Source Code Authorship Attribution (SCAA) is a direct challenge to the privacy and anonymity of developers. However, it is important to recognize the malicious authors and the origin of the attack. In this paper, we proposed Source Code Authorship Attribution using Abstract Syntax Tree (SCAA-AST) for efficient classification of programmers. First, the AST hierarchal features are generated from different programming codes. Second, preprocessing techniques are used to obtain useful features without sound data. Third, the Term Frequency Inverse Document Frequency (TFIDF) weighting technique is used to zoom in on the significance of each feature. Fourth, the Adaptive Synthetic (ADASYN) oversampling method is used to solve the imbalanced class problem. Finally, a deep learning algorithm is designed with the TensorFlow framework, and the Keras API is used to classify programming authors. A deep learning algorithm is further configured with a dropout layer, learning error rate, loss and activation function, and dense layers to enhance the classification results. The results are appreciable in outperforming the existing techniques from the perspective of classification accuracy.

Suggested Citation

  • Ullah, Farhan & Jabbar, Sohail & Al-Turjman, Fadi, 2020. "Programmers' de-anonymization using a hybrid approach of abstract syntax tree and deep learning," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
  • Handle: RePEc:eee:tefoso:v:159:y:2020:i:c:s004016252031012x
    DOI: 10.1016/j.techfore.2020.120186
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252031012X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2020.120186?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Muhammad Aliyu & Murali M & Abdulsalam Y. Gital & Souley Boukari, 2020. "Efficient Metaheuristic Population-Based and Deterministic Algorithm for Resource Provisioning Using Ant Colony Optimization and Spanning Tree," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 10(2), pages 1-21, April.
    2. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gabriela Czibula & Mihaiela Lupea & Anamaria Briciu, 2022. "Enhancing the Performance of Software Authorship Attribution Using an Ensemble of Deep Autoencoders," Mathematics, MDPI, vol. 10(15), pages 1-27, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nils-Axel M?rner, 2018. "Evaluation of the Performance and Efficiency of the Automated Linguistic Features for Author Identification in Short Text Messages Using Different Variable Selection Techniques," Studies in Media and Communication, Redfame publishing, vol. 6(2), pages 83-102, December.
    2. Maryam Ebrahimpour & Tālis J Putniņš & Matthew J Berryman & Andrew Allison & Brian W-H Ng & Derek Abbott, 2013. "Automated Authorship Attribution Using Advanced Signal Classification Techniques," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    3. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    4. Rajakumar, R. & Sekaran, Kaushik & Hsu, Ching-Hsien & Kadry, Seifedine, 2021. "Accelerated grey wolf optimization for global optimization problems," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    5. Sanda-Maria Avram & Mihai Oltean, 2022. "A Comparison of Several AI Techniques for Authorship Attribution on Romanian Texts," Mathematics, MDPI, vol. 10(23), pages 1-35, December.
    6. Ballandonne, Matthieu & Cersosimo, Igor, 2022. "Towards a “Text as Data” Approach in the History of Economics: An Application to Adam Smith’s Classics," OSF Preprints mg3zb, Center for Open Science.
    7. Malik Muhammad Saad Missen & Sajeeha Qureshi & Nadeem Salamat & Nadeem Akhtar & Hina Asmat & Mickaël Coustaty & V. B. Surya Prasath, 2020. "Scientometric analysis of social science and science disciplines in a developing nation: a case study of Pakistan in the last decade," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 113-142, April.
    8. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    9. Kargin, Vladislav, 2016. "On variation of word frequencies in Russian literary texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 328-334.
    10. Andi Rexha & Mark Kröll & Hermann Ziak & Roman Kern, 2018. "Authorship identification of documents with high content similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 223-237, April.
    11. Jacques Savoy & Olena Zubaryeva, 2012. "Simple and efficient classification scheme based on specific vocabulary," Computational Management Science, Springer, vol. 9(3), pages 401-415, August.
    12. Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.
    13. Silvia Corbara & Alejandro Moreo & Fabrizio Sebastiani, 2023. "Syllabic quantity patterns as rhythmic features for Latin authorship attribution," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 128-141, January.
    14. Oleg Sobchuk & Artjoms Šeļa, 2024. "Computational thematics: comparing algorithms for clustering the genres of literary fiction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    15. Jennifer A. Byrne & Cyril Labbé, 2017. "Striking similarities between publications from China describing single gene knockdown experiments in human cancer cell lines," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1471-1493, March.
    16. de Arruda, Henrique F. & Marinho, Vanessa Q. & Lima, Thales S. & Amancio, Diego R. & Costa, Luciano da F., 2018. "An image analysis approach to text analytics based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 110-120.
    17. Stefano Sbalchiero & Maria Stella Righettini, 2017. "Rhetorical manifestation of institutional transformation," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1279-1296, May.
    18. Mihailo Škorić & Ranka Stanković & Milica Ikonić Nešić & Joanna Byszuk & Maciej Eder, 2022. "Parallel Stylometric Document Embeddings with Deep Learning Based Language Models in Literary Authorship Attribution," Mathematics, MDPI, vol. 10(5), pages 1-27, March.
    19. Matilde Trevisani & Arjuna Tuzzi, 2015. "A portrait of JASA: the History of Statistics through analysis of keyword counts in an early scientific journal," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1287-1304, May.
    20. Catalin Stoean & Daniel Lichtblau, 2020. "Author Identification Using Chaos Game Representation and Deep Learning," Mathematics, MDPI, vol. 8(11), pages 1-18, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:159:y:2020:i:c:s004016252031012x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.