On the Practicability of Deep Learning based Anomaly Detection for Modern Online Systems: A Pre-Train-and-Align Framework
This work extends upon our previous work ``Share or Not Share? Towards the Practicability of Deep Models for Unsupervised Anomaly Detection in Modern Online Systems'' at the 33rd IEEE International Symposium on Software Reliability Engineering (ISSRE 2022), which is also honored to get the best paper award in the research track.
We are currently refactoring the code, including a library rename and a more easy-to-use interface to apply new techniques of our work. If you find any issues, please feel free to e-mail us.
git clone https://github.com/IntelligentDDS/ShareAD.git
You can get the public datasets from:
- CTF_data: https://github.com/NetManAIOps/CTF_data
- SMD: https://github.com/NetManAIOps/OmniAnomaly
- JS_D2+D3: https://github.com/NetManAIOps/JumpStarter
Here, since we focus on anomaly detection in modern large-scale online systems, we may prioritize CTF_data.
pip install -r requirements.txt
An example using the CTF_data is provided in the notebook example.ipynb
.