Robust Random Cut Forest Based Anomaly Detection on Streams
Abstract
In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests. We investigate a robust random cut data structure that can be used as a sketch or synopsis of the input stream. We provide a plausible definition of non-parametric anomalies based on the influence of an unseen point on the remainder of the data, i.e., the externality imposed by that point. We show how the sketch can be efficiently updated in a dynamic data stream. We demonstrate the viability of the algorithm on publicly available real data.
Cite
Text
Guha et al. "Robust Random Cut Forest Based Anomaly Detection on Streams." International Conference on Machine Learning, 2016.Markdown
[Guha et al. "Robust Random Cut Forest Based Anomaly Detection on Streams." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/guha2016icml-robust/)BibTeX
@inproceedings{guha2016icml-robust,
title = {{Robust Random Cut Forest Based Anomaly Detection on Streams}},
author = {Guha, Sudipto and Mishra, Nina and Roy, Gourav and Schrijvers, Okke},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {2712-2721},
volume = {48},
url = {https://mlanthology.org/icml/2016/guha2016icml-robust/}
}