Academic Journal
Plagiarism Detection of Multi-Threaded Programs via Siamese Neural Networks
| العنوان: | Plagiarism Detection of Multi-Threaded Programs via Siamese Neural Networks |
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| المؤلفون: | Zhenzhou Tian, Qing Wang, Cong Gao, Lingwei Chen, Dinghao Wu |
| المصدر: | IEEE Access, Vol 8, Pp 160802-160814 (2020) |
| بيانات النشر: | IEEE, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: | Software plagiarism detection, multi-threaded programs, dynamic birthmark, semantic behaviors, deep learning, siamese neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: | Widespread intentional or unintentional software plagiarisms have posed serious threats to the healthy development of software industry. In order to detect such evolving software plagiarism, software dynamic birthmark techniques of better anti-obfuscation ability serve as one of the most promising methods. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, existing dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. Some thread-aware birthmarking methods have been then proposed to address this issue, which nevertheless largely rely on manual feature engineering and empirical observations without any ground-truth training, and thus require domain knowledge, making them inflexible to be deployed in the wild. Inspired by the success of self-guided optimization using deep neural networks and their superior feature learning ability, in this article, we transform multiple execution traces for each multi-threaded program under a specified input to the plain feature matrix, and feed it to the deep learning framework to learn latent representation as thread-aware birthmark that enjoys better semantic richness and perturbation resistance; instead of empirically determining the plagiarism over direct birthmark similarity metric, we further build up sophisticated siamese neural networks to supervise birthmark construction, similarity measurement, and decision making. Integrating our proposed method, a system called NeurMPD is developed to perform Neural network-based Multi-threaded program Plagiarism Detection. The experimental results based on a public software plagiarism sample set demonstrate that NeurMPD copes better with multi-threaded plagiarism detection than alternative approaches. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2169-3536 65019547 |
| Relation: | https://ieeexplore.ieee.org/document/9184827/; https://doaj.org/toc/2169-3536 |
| DOI: | 10.1109/ACCESS.2020.3021184 |
| URL الوصول: | https://doaj.org/article/f7c91b0aa22f4a0d97a65019547331dd |
| رقم الانضمام: | edsdoj.f7c91b0aa22f4a0d97a65019547331dd |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21693536 65019547 |
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| DOI: | 10.1109/ACCESS.2020.3021184 |