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Security pattern detection in software code using machine learning algorithms

Author:
Cha, Joonyoung
Published:
[University Park, Pennsylvania] : Pennsylvania State University, 2022.
Physical Description:
1 electronic document
Additional Creators:
Ryoo, Jungwoo
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Restricted (PSU Only).
Summary:
Security patterns, defined as reusable building blocks of secure software code architecture, provide solutions to recurring security flaws and problems in specific contexts. Implementing non-standard or incomplete security patterns may create vulnerabilities that cybercriminals can exploit to execute various attacks on a computer system. Security patterns must be accurately identified and used to enhance software code quality and security features. This study examines the possibility of using machine learning algorithms to detect security patterns in software code. The proposed framework for our research is the Security Pattern Detection (SPD) and its internal pattern matching technique, Non-uniform Distributed Matrix Matching (NDMM). The machine learning algorithms selected for our study are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The primary data for the study were collected by interviewing experts who agreed to participate in the study. The purposive sampling method was used to select experts in machine learning algorithms and security pattern detection in software code. The experts' responses were analyzed and, in conjunction with findings from recent studies on CNN and LSTM, used to develop a comprehensive discussion of the prospect of using machine learning algorithms to detect security patterns in software code.
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Dissertation Note:
M.S. Pennsylvania State University 2022.
Technical Details:
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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