Towards Experienced Anomaly Detector Through Reinforcement Learning
Abstract
This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.
Cite
Text
Huang et al. "Towards Experienced Anomaly Detector Through Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12130Markdown
[Huang et al. "Towards Experienced Anomaly Detector Through Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/huang2018aaai-experienced/) doi:10.1609/AAAI.V32I1.12130BibTeX
@inproceedings{huang2018aaai-experienced,
title = {{Towards Experienced Anomaly Detector Through Reinforcement Learning}},
author = {Huang, Chengqiang and Wu, Yulei and Zuo, Yuan and Pei, Ke and Min, Geyong},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {8087-8088},
doi = {10.1609/AAAI.V32I1.12130},
url = {https://mlanthology.org/aaai/2018/huang2018aaai-experienced/}
}