logpai/deep-loglizerA deep learning toolkit for log-based anomaly detection
Deep-loglizer is a deep learning-based log analysis toolkit for automated anomaly detection.
If you use deep-loglizer in your research for publication, please kindly cite the following paper:
| Model | Paper reference |
|---|---|
| Unsupervised models | |
| LSTM | [CCS'17] Deeplog: Anomaly detection and diagnosis from system logs through deep learning, by Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. [University of Utah] |
| LSTM | [IJCAI'19] LogAnomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs by Weibin Meng, Ying Liu, Yichen Zhu et al. [Tsinghua University] |
| Transformer | [ICDM'20] Self-attentive classification-based anomaly detection in unstructured logs, by Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, and Odej Kao. [TU Berlin] |
| Autoencoder | [ICT Express'20] Unsupervised log message anomaly detection, by Amir Farzad and T Aaron Gulliver. [University of Victoria] |
| Supervised models | |
| Attentional BiLSTM | [ESEC/FSE'19] Robust log-based anomaly detection on unstable log data by Xu Zhang, Yong Xu, Qingwei Lin et al. [MSRA] |
| CNN | [DASC'18] Detecting anomaly in big data system logs using convolutional neural network by Siyang Lu, Xiang Wei, Yandong Li, and Liqiang Wang. [University of Central Florida] |
git clone https://github.com/logpai/deep-loglizer.git
cd deep-loglizer
pip install -r requirements.txt