On the Applicability of Deep Metric Learning to Address Source Code Authorship Attribution Problem Under Simulated Real-World Constraints
نام عام مواد
[Thesis]
نام نخستين پديدآور
Zafar, Sarim
نام ساير پديدآوران
Malik, Muhammad Zubair
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
North Dakota State University
تاریخ نشرو بخش و غیره
2020
يادداشت کلی
متن يادداشت
101 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
North Dakota State University
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Source code authorship attribution is a widely studied research topic in the information security domain. In this dissertation, we develop and evaluate models that enable us to solve source code authorship attribution using deep metric learning. In particular, first, we simulate a real-world setting. Second, we use a number of loss functions from the deep metric learning domain to train neural network models. Thirdly, we evaluate these different models' performance on a benchmark and determine whether there is a quantifiable performance difference between these deep metric loss functions. Lastly, we demonstrate how we can extend our proposed methodology address the open world scenario. We argue that these models, and the techniques they take advantage of, are a stepping stone towards achieving real-world source code authorship attribution that can work across multiple programming languages and even under large scale obfuscated settings.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Artificial intelligence
اصطلاح موضوعی
Computer science
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )