Online Isolation Forest
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
@article{arxiv.2505.09593,
title = {Online Isolation Forest},
author = {Filippo Leveni and Guilherme Weigert Cassales and Bernhard Pfahringer and Albert Bifet and Giacomo Boracchi},
journal= {arXiv preprint arXiv:2505.09593},
year = {2025}
}
Comments
Accepted at International Conference on Machine Learning (ICML 2024)