Machine Learning For Vulnerability Detection
Machine Learning for Vulnerability Detection is a computational methodology within static application security testing that employs machine learning models to automatically identify software vulnerabilities by learning patterns from historical codebases and vulnerability datasets.
310
Publications
16.1K
Citations
1K
Authors
416
Institutions
Publications per year
2017–2026
275
310
Software Vulnerability Detection Using Deep Neural Networks: A Survey
Guanjun Lin, Sheng Wen, Qing‐Long Han et al. · Proceedings of the IEEE · 2020 · 440 citations
Smart Contract Vulnerability Detection using Graph Neural Network
Yuan Zhuang, Zhenguang Liu, Peng Qian et al. · 2020 · 376 citations · Full text
Predicting Vulnerable Software Components via Text Mining
Riccardo Scandariato, James Walden, Aram Hovsepyan et al. · IEEE Transactions on Software Engineering · 2014 · 337 citations · Full text
1–5 of 310
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416
Nanyang Technological University
Singapore, Singapore
10 h-index
40 publications
1.5K citations
Singapore Management University
Singapore, Singapore
9 h-index
28 publications
1K citations

Yangzhou University
Yangzhou, China
7 h-index
26 publications
2.2K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
15 publications
767 citations

Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
19 publications
694 citations
University of Luxembourg
Luxembourg, Luxembourg
6 h-index
22 publications
1.3K citations
Swinburne University of Technology
Melbourne, Australia
5 h-index
21 publications
3.3K citations

Sichuan University
Chengdu, China
5 h-index
21 publications
871 citations
1–8 of 416
Repository · 12 publications · 411 citations
11
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11 publications · 552 citations
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8 publications · 189 citations
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9 publications · 248 citations
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JSS · 6 publications · 391 citations
6
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