On the Applicability of Deep Metric Learning to Address Source Code Authorship Attribution Problem U...

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" On the Applicability of Deep Metric Learning to Address Source Code Authorship Attribution Problem Under Simulated Real-World Constraints "
Zafar, Sarim Malik, Muhammad Zubair

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1056541
Doc. No	:	TL55658
Main Entry	:	Zafar, Sarim
Title & Author	:	On the Applicability of Deep Metric Learning to Address Source Code Authorship Attribution Problem Under Simulated Real-World Constraints\ Zafar, SarimMalik, Muhammad Zubair
College	:	North Dakota State University
Date	:	2020
Degree	:	M.S.
student score	:	2020
Note	:	101 p.
Abstract	:	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.
Descriptor	:	Artificial intelligence
	:	Computer science
Added Entry	:	Malik, Muhammad Zubair
Added Entry	:	North Dakota State University