Sequence-based Malware Classification
Sequence-Based Malware Classification is a malware classification approach that distinguishes itself by analyzing sequential representations of code or behavior—such as instruction sequences, system call traces, or opcode streams—using pattern recognition or machine learning techniques to identify malicious characteristics based on order, structure, and temporal dependencies.
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129
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51
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2017–2026
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Deep learning at the shallow end: Malware classification for non-domain experts
Quan Le, Oisín Boydell, Brian Mac Namee et al. · Digital Investigation · 2018 · 191 citations · Full text
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Southern Methodist University
Dallas, United States
2 h-index
4 publications
256 citations

University of Tulsa
Tulsa, United States
2 h-index
2 publications
128 citations
Institute of Information Engineering
Beijing, China
2 h-index
8 publications
187 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
148 citations
Physical Sciences (United States)
Andover, United States
1 h-index
6 publications
966 citations

The University of Sydney
Sydney, Australia
1 h-index
2 publications
60 citations
Vellore Institute of Technology University
Vellore, India
1 h-index
1 publications
61 citations
RMIT University
Melbourne, Australia
1 h-index
2 publications
60 citations
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3 publications · 251 citations
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Repository · 3 publications · 68 citations
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2 publications · 144 citations
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JISA · 1 publications · 27 citations
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Repository · 1 publications · 12 citations
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