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Deep-loglizer

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:

Framework

Deep Learning-based Log Anomaly Detection

Models

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]

Install

git clone https://github.com/logpai/deep-loglizer.git
cd deep-loglizer
pip install -r requirements.txt

Contributors

  • Zhuangbin Chen, The Chinese University of Hong Kong
  • Jinyang Liu, The Chinese University of Hong Kong
  • Wenwei Gu, The Chinese University of Hong Kong

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A deep learning toolkit for log-based anomaly detection

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