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Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 1 (2022) · pp. 70–87

DOI
10.5614/itbj.ict.res.appl.2022.16.1.5
ISSN
2338-5499, 2337-5787
Published
2022-05-11
Publisher
DRI - ITB
Language
eng

Abstract

Nowadays, software vulnerabilities pose a serious problem, because cyber-attackers often find ways to attack a system by exploiting software vulnerabilities. Detecting software vulnerabilities can be done using two main methods: i) signature-based detection, i.e. methods based on a list of known security vulnerabilities as a basis for contrasting and comparing; ii) behavior analysis-based detection using classification algorithms, i.e., methods based on analyzing the software code. In order to improve the ability to accurately detect software security vulnerabilities, this study proposes a new approach based on a technique of analyzing and standardizing software code and the random forest (RF) classification algorithm. The novelty and advantages of our proposed method are that to determine abnormal behavior of functions in the software, instead of trying to define behaviors of functions, this study uses the Word2vec natural language processing model to normalize and extract features of functions. Finally, to detect security vulnerabilities in the functions, this study proposes to use a popular and effective supervised machine learning algorithm.

Keywords

Collaboration network of the authors

3 authors · 3 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • FPT University
  • Posts and Telecommunications Institute of Technology
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms</dc:title>
	<dc:creator>Cho, Do Xuan</dc:creator>
	<dc:creator>Son, Vu Ngoc</dc:creator>
	<dc:creator>Duc, Duong</dc:creator>
	<dc:subject xml:lang="en-US">machine learning algorithms</dc:subject>
	<dc:subject xml:lang="en-US">natural language processing techniques</dc:subject>
	<dc:subject xml:lang="en-US">software security vulnerability detection</dc:subject>
	<dc:subject xml:lang="en-US">software vulnerabilities</dc:subject>
	<dc:subject xml:lang="en-US">source code features</dc:subject>
	<dc:description xml:lang="en-US">Nowadays, software vulnerabilities pose a serious problem, because cyber-attackers often find ways to attack a system by exploiting software vulnerabilities. Detecting software vulnerabilities can be done using two main methods: i) signature-based detection, i.e. methods based on a list of known security vulnerabilities as a basis for contrasting and comparing; ii) behavior analysis-based detection using classification algorithms, i.e., methods based on analyzing the software code. In order to improve the ability to accurately detect software security vulnerabilities, this study proposes a new approach based on a technique of analyzing and standardizing software code and the random forest (RF) classification algorithm. The novelty and advantages of our proposed method are that to determine abnormal behavior of functions in the software, instead of trying to define behaviors of functions, this study uses the Word2vec natural language processing model to normalize and extract features of functions. Finally, to detect security vulnerabilities in the functions, this study proposes to use a popular and effective supervised machine learning algorithm.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-05-11</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/16905</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.1.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 1 (2022); 70-87</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/16905/5786</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of ICT Research and Applications</dc:rights>
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