ICML 2016poster458 citations
Robust Random Cut Forest Based Anomaly Detection on Streams
Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers
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.
BibTeX
@InProceedings{pmlr-v48-guha16,
title = {Robust Random Cut Forest Based Anomaly Detection on Streams},
author = {Guha, Sudipto and Mishra, Nina and Roy, Gourav and Schrijvers, Okke},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {2712--2721},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/guha16.pdf},
url = {https://proceedings.mlr.press/v48/guha16.html},
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.}
}