XIAOXIOA881 / LogTAD

LogTAD: Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation (CIKM 2021)

LogTAD: Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation

A Pytorch implementation of LogTAD.

Configuration

• Ubuntu 20.04
• NVIDIA driver 460.73.01
• CUDA 11.2
• Python 3.9
• PyTorch 1.9.0

Installation

This code requires the packages listed in requirements.txt. A virtual environment is recommended to run this code

On macOS and Linux:

python3 -m pip install --user virtualenv
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
deactivate

Reference: https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/

Instructions

LogTAD and other baseline models are implemented on BGL and Thunderbird datasets

Clone the template project, replacing my-project with the name of the project you are creating:

    git clone https://github.com/hanxiao0607/LogTAD.git my-project
    cd my-project

Run and test:

    python3 main_LogTAD.py

Citation

@inproceedings{han2021unsupervised,
  title={Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation},
  author={Han, Xiao and Yuan, Shuhan},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  pages={3068--3072},
  year={2021}
}