Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms

Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms

machine learning algorithms; natural language processing techniques;
software security vulnerability detection; software vulnerabilities; source code features.

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.

Cho Do Xuan, Vu Ngoc Son & Duong Duc

DOI: 10.5614/itbj.ict.res.appl.2022.16.1.5

IRCS-ITB

01 Desember 2021

Sri Wahyuni

ISSN: 2337-5787

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Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022