1. 日志异常检测
1.1 日志异常检测模型
- 深度模型的日志异常检测，还有谁不会？：https://segmentfault.com/a/1190000039060586
- AIOps关键技术：FT-tree提取日志模板 : https://cloud.tencent.com/developer/news/238966
- (小米智能运维团队) 日志异常检测初步实践与探索: https://blog.csdn.net/pengzhouzhou/article/details/110211666#comments_19093723
1.2 Toolkits
-
Logparser：https://github.com/logpai/logparser
Logparser provides a toolkit and benchmarks for automated log parsing, which is a crucial step towards structured log analytics. By applying logparser, users can automatically learn event templates from unstructured logs and convert raw log messages into a sequence of structured events. In the literature, the process of log parsing is sometimes refered to as message template extraction, log key extraction, or log message clustering. -
Loglizer：https://github.com/logpai/loglizer
loglizer is a machine learning-based log analysis toolkit for automated anomaly detection.
Loglizer是一款基于AI的日志大数据分析工具, 能用于自动异常检测、智能故障诊断等场景
1.3 Awesome Log Analysis
A curated list of awesome publications and researchers on log analysis, anomaly detection, fault localization, and AIOps.
https://github.com/logpai/awesome-log-analysis
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