intelligentdds / sharead

On the Practicability of Deep Learning based Anomaly Detection for Modern Online Systems: A Pre-Train-and-Align Framework

ShareAD

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.

Note

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.

Start

Clone the repository

git clone https://github.com/IntelligentDDS/ShareAD.git

Get data

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.

Install dependencies (with python 3.7.6)

pip install -r requirements.txt

Running an Example

An example using the CTF_data is provided in the notebook example.ipynb.