This repository is the unofficial implementation of Self-attentive classification-based anomaly detection in unstructured logs.
๐ Please find a demo Colab notebook at the src folder at project root
To install requirements locally and run notebook locally, verify the dependencies in the requirements.txt
:
pip install -r requirements.txt
When using our implementation demo, simply import the notebook at src/model/anomaly_detection.ipynb
and modify the folder path to point to your datasets.
Baselines: We implemented two baselines used in the paper - PCA and Deeplog. Please refer to corresponding notebooks for their specifics.
To train the model(s) in the paper, import the notebook with TPU runtime and parallel execution strategy on, each epoch at batch size 512 will take less than 2 mins for first 5 million rows of data.
The results can be evaluated by observing the F1-score, Recall, Precision and Accuracy. The threshold derivation is automatically iterated and can be observed.
Please review the results based on our project report [NOT DISCLOSED FOR NOW].
Generally we have evidence to prove that the results are reproduciable (also surpassing previous state-of-the-art DeepLog) with some potential evaluation flaws.
If you want to run PCA yourself, please:
cd baselines/PCA/code
python main.py
If you want to run Deeplog:
cd baselines/Deeplog/code
python main.py
@article{nedelkoski2020self,
title={Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs},
author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Acker, Alexander and Cardoso, Jorge and Kao, Odej},
journal={arXiv preprint arXiv:2008.09340},
year={2020}
}
Please click on the button Cite this repository
below the repo description. A bibitex will be generated for your convinience.
This code is released under GPLV3 License.
Pull requests and issues are welcomed to enhance the implementation.