Author: Wong, Ying Kit
Title: Transformer approach on source code vulnerability detection
Degree: Eng.D.
Year: 2023
Subject: Computer software -- Quality control
Debugging in computer science
Computer security
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Engineering
Pages: 1 volume (unpaged) : color illustrations
Language: English
Abstract: Software quality is a major concern in the software business. Security attacks continue to escalate, leading to application failures, financial losses, and erosion of confidence. Therefore, it is critical to allow developers to discover security flaws in source code before deployment. Several static analysis techniques have been created to uncover security issues in source code. However, traditional approaches have not had a leveraged machine learning mechanism to reduce operational effort and improve detection performance.
Vulnerability identification is a crucial task in security. With the development of technology, identifying vulnerable source code has become a hot topic in the industry in order to handle large code bases. Applying natural language processing (NLP) to source code and building models to complete the analysis and digestion of source code has become one of the valuable vulnerability identification studies. Detecting errors before they cause damage is the desire of The solution. Previous efforts have usually been unsuccessful and inefficient.
This paper contributes an enhanced approach to detecting errors using the Transformer method in deep learning. The method is validated against a vulnerability benchmark database. We employ NLP to process source code as a sequence of text in a specific domain and embed the text by paying attention to a highly relevant portion of the code to solve the error detection problem with a pre-trained and then fine-tuned model.
Rights: All rights reserved
Access: restricted access

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