The OC4Seq repository contains code for a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences. The method implemented here is described in this paper.

• exp.py: This is the main experiment script
• model.py: The OC4Seq is implemented in the this file
• trainer.py: It implements the training producedure
• utils.py: It preprocesses the data for experiment

• Zhiwei Wang
• Zhengzhang Chen
• Jingchao Ni
• Hui Liu
• Haifeng Chen
• Jiliang Tang

If you find the code in this respository useful for your research, please cite our paper:

  @inproceedings{WangCNLCT21,
  author    = {Zhiwei Wang and
               Zhengzhang Chen and
               Jingchao Ni and
               Hui Liu and
               Haifeng Chen and
               Jiliang Tang},
  title     = {Multi-Scale One-Class Recurrent Neural Networks for Discrete Event
               Sequence Anomaly Detection},
  booktitle = {{KDD} '21: The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery
               and Data Mining, Virtual Event, Singapore, August 14-18, 2021},
  pages     = {3726--3734},
  publisher = {{ACM}},
  year      = {2021}
}

Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 International License

The OC4Seq is released under a CC BY-NC 4.0 International License: https://creativecommons.org/licenses/by-nc/4.0.

NonCommercial — You can not use the code for commercial purposes.