LogIntelligence/LogADEmpirical
Log-based Anomaly Detection with Deep Learning: How Far Are We? (ICSE 2022, Technical Track)
This project provides tools for evaluating how well deep learning models detect system anomalies from log data. It helps you feed system logs into various deep learning models and assess their accuracy and effectiveness under different conditions. Site reliability engineers, system administrators, or software researchers can use this to understand the true performance of anomaly detection systems.
215 stars. No commits in the last 6 months.
Use this if you need to rigorously test and compare different deep learning models for identifying unusual patterns or errors within your system's log files.
Not ideal if you're looking for a production-ready, out-of-the-box anomaly detection system rather than a research and evaluation framework.
Stars
215
Forks
48
Language
Python
License
MIT
Category
Last pushed
Sep 27, 2024
Commits (30d)
0
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