Machine Learning For Traffic Analysis
Machine Learning for Traffic Analysis is a computational methodology that employs machine learning algorithms to automatically detect, classify, and predict network traffic patterns by learning from empirical traffic data, thereby enabling intelligent inference and decision-making in network monitoring, anomaly detection, and performance optimization.
1.9K
Publications
106.4K
Citations
5.8K
Authors
1.6K
Institutions
Publications per year
2017–2026
1.3K
Parents
1.9K
Internet traffic classification using bayesian analysis techniques
Andrew W. Moore · 2005 · 1.2K citations
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Tsinghua University
Beijing, China
32 h-index
136 publications
8.7K citations

Chinese Academy of Sciences
Beijing, China
29 h-index
127 publications
23.1K citations

Beijing University of Posts and Telecommunications
Beijing, China
22 h-index
126 publications
7.6K citations
University of Chinese Academy of Sciences
Beijing, China
22 h-index
102 publications
7.7K citations

Southeast University
Nanjing, China
22 h-index
101 publications
4.2K citations
Swinburne University of Technology
Melbourne, Australia
18 h-index
50 publications
9.6K citations

University of Naples Federico II
Naples, Italy
18 h-index
71 publications
5.6K citations
Institute of Information Engineering
Beijing, China
16 h-index
80 publications
8K citations
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84 publications · 4.5K citations
42
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46 publications · 6.6K citations
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TNSM · 33 publications · 2K citations
26
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23 publications · 3.1K citations
23
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38 publications · 1.4K citations
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