[ICDM 2020] Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

This is the implementation of ICDM 2020 paper Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. We propose to learn a meta-policy with deep reinforcement learning to optimize the performance of active anomaly detection. Please refer the paper for more deteails.

Cite this Work

If you find this project helpful, please cite

@inproceedings{DBLP:conf/icdm/ZhaLWH20,
  author    = {Daochen Zha and
               Kwei{-}Herng Lai and
               Mingyang Wan and
               Xia Hu},
  editor    = {Claudia Plant and
               Haixun Wang and
               Alfredo Cuzzocrea and
               Carlo Zaniolo and
               Xindong Wu},
  title     = {Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning},
  booktitle = {20th {IEEE} International Conference on Data Mining, {ICDM} 2020,
               Sorrento, Italy, November 17-20, 2020},
  pages     = {771--780},
  publisher = {{IEEE}},
  year      = {2020},
  url       = {https://doi.org/10.1109/ICDM50108.2020.00086},
  doi       = {10.1109/ICDM50108.2020.00086},
  timestamp = {Wed, 17 Feb 2021 11:24:58 +0100},
  biburl    = {https://dblp.org/rec/conf/icdm/ZhaLWH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Installation

Make sure you have python 3.5+ installed.

git clone https://github.com/daochenzha/Meta-AAD.git
cd Meta-AAD
pip install -r requirments.txt
pip install -e .

Training a Meta-Policy

Train a meta-policy with train.py. The important arguments are as follows.

• --train: the datasets used for training, seperated by commas.
• --test: the datasets used for testing, seperated by commas.
• --num_timesteps: the number of training steps of reinforcement learning agents
• --log: where the log and models will be outputted

By default, the reinforcement learning training log will be saved in log/, the anomaly discovery curves will be saved in log/anomaly_curves, and the trained model will be saved in log/.

Evaluating a Trained Model

You may evaluate a trained model with evaluate.py. The important arguments are as follows.

• --load: the path to model.zip file.
• --test: the datasets used for testing, seperated by commas.

Baselines

We provide two baselines in this repo for comparison: a random query strategy and IForest query strategy. They are available in evaluate_baselines.py. For other baselines, please refer to the following repos.

• AAD: https://github.com/shubhomoydas/ad_examples
• FIF: https://github.com/siddiqmd/FeedbackIsolationForest
• SSDO: https://github.com/Vincent-Vercruyssen/anomatools