Log-based Detection
Log-Based Detection is a method in incident detection that identifies security incidents by analyzing log data generated by systems, applications, and network devices to detect anomalous or malicious activities through pattern recognition and correlation of recorded events.
309
Publications
17K
Citations
1.1K
Authors
355
Institutions
Publications per year
2017–2026
153
Parents
309
Detecting large-scale system problems by mining console logs
Wei Xu, Ling Huang, Armando Fox et al. · 2009 · 1.3K citations
Robust log-based anomaly detection on unstable log data
Xu Zhang, Yong Xu, Qingwei Lin et al. · 2019 · 674 citations
What Supercomputers Say: A Study of Five System Logs
Adam J. Oliner, Jon Stearley · 2007 · 608 citations
Experience Report: System Log Analysis for Anomaly Detection
Shilin He, Jieming Zhu, Pinjia He et al. · 2016 · 567 citations
A data clustering algorithm for mining patterns from event logs
Risto Vaarandi · 2004 · 457 citations
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355
Microsoft (United States)
Redmond, United States
7 h-index
19 publications
5.6K citations

Nankai University
Tianjin, China
5 h-index
18 publications
432 citations

Concordia University
Montreal, Canada
5 h-index
10 publications
291 citations
Tsinghua University
Beijing, China
5 h-index
14 publications
787 citations

Sun Yat-sen University
Guangzhou, China
5 h-index
16 publications
612 citations

University of Naples Federico II
Naples, Italy
5 h-index
11 publications
193 citations
Microsoft Research Asia (China)
Beijing, China
5 h-index
25 publications
8.3K citations
University of Michigan
Ann Arbor, United States
4 h-index
8 publications
1.5K citations
1–8 of 355
Repository · 6 publications · 144 citations
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5 publications · 83 citations
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Conference · 4 publications · 520 citations
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TDSC · 3 publications · 126 citations
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3 publications · 80 citations
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