Entropy-based Classification
Entropy-Based Classification is a malware classification method that distinguishes malicious software based on the statistical entropy of file content, wherein high entropy values typically indicate encryption or packing behaviors characteristic of obfuscated malware.
276
Publications
13.6K
Citations
881
Authors
414
Institutions
Publications per year
2017–2026
129
Parents
276
Using Maximum Entropy for Text Classification
Kamal Nigam, John Lafferty, Andrew McCallum · 1999 · 757 citations
1–5 of 276
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Valencia Catholic University Saint Vincent Martyr, Spain
3 publications · 90 citations
3
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3
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1–5 of 881
414
Universitat Politècnica de València
Valencia, Spain
6 h-index
15 publications
275 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
13 publications
351 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
12 publications
1.8K citations
Korea University
Seoul, South Korea
4 h-index
15 publications
560 citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
5 publications
331 citations
Texas A&M University
College Station, United States
4 h-index
7 publications
222 citations
University of Science and Technology of China
Hefei, China
3 h-index
6 publications
218 citations
Michigan State University
East Lansing, United States
3 h-index
6 publications
203 citations
1–8 of 414
22 publications · 700 citations
16
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6 publications · 323 citations
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6 publications · 155 citations
6
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5 publications · 292 citations
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4 publications · 138 citations
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