Online Isolation Forest

Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer, Albert Bifet, Giacomo Boracchi
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:27288-27298, 2024.

Abstract

The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming context. Existing online anomaly detection methods also generally fail to address these constraints, resorting to periodic retraining to adapt to the online context. We propose Online-iForest, a novel method explicitly designed for streaming conditions that seamlessly tracks the data generating process as it evolves over time. Experimental validation on real-world datasets demonstrated that Online-iForest is on par with online alternatives and closely rivals state-of-the-art offline anomaly detection techniques that undergo periodic retraining. Notably, Online-iForest consistently outperforms all competitors in terms of efficiency, making it a promising solution in applications where fast identification of anomalies is of primary importance such as cybersecurity, fraud and fault detection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-leveni24a, title = {Online Isolation Forest}, author = {Leveni, Filippo and Weigert Cassales, Guilherme and Pfahringer, Bernhard and Bifet, Albert and Boracchi, Giacomo}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {27288--27298}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/leveni24a/leveni24a.pdf}, url = {https://proceedings.mlr.press/v235/leveni24a.html}, abstract = {The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming context. Existing online anomaly detection methods also generally fail to address these constraints, resorting to periodic retraining to adapt to the online context. We propose Online-iForest, a novel method explicitly designed for streaming conditions that seamlessly tracks the data generating process as it evolves over time. Experimental validation on real-world datasets demonstrated that Online-iForest is on par with online alternatives and closely rivals state-of-the-art offline anomaly detection techniques that undergo periodic retraining. Notably, Online-iForest consistently outperforms all competitors in terms of efficiency, making it a promising solution in applications where fast identification of anomalies is of primary importance such as cybersecurity, fraud and fault detection.} }
Endnote
%0 Conference Paper %T Online Isolation Forest %A Filippo Leveni %A Guilherme Weigert Cassales %A Bernhard Pfahringer %A Albert Bifet %A Giacomo Boracchi %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-leveni24a %I PMLR %P 27288--27298 %U https://proceedings.mlr.press/v235/leveni24a.html %V 235 %X The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming context. Existing online anomaly detection methods also generally fail to address these constraints, resorting to periodic retraining to adapt to the online context. We propose Online-iForest, a novel method explicitly designed for streaming conditions that seamlessly tracks the data generating process as it evolves over time. Experimental validation on real-world datasets demonstrated that Online-iForest is on par with online alternatives and closely rivals state-of-the-art offline anomaly detection techniques that undergo periodic retraining. Notably, Online-iForest consistently outperforms all competitors in terms of efficiency, making it a promising solution in applications where fast identification of anomalies is of primary importance such as cybersecurity, fraud and fault detection.
APA
Leveni, F., Weigert Cassales, G., Pfahringer, B., Bifet, A. & Boracchi, G.. (2024). Online Isolation Forest. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:27288-27298 Available from https://proceedings.mlr.press/v235/leveni24a.html.

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