Leveni, FCassales, GuilhermePfahringer, BernhardBifet, AlbertBoracchi, G2024-10-312024-10-312024Leveni, F., Weigert Cassales, G., Pfahringer, B., Bifet, A., & Boracchi, G. (2024, July 21-27). Online Isolation Forest [Conference item]. Forty-first International Conference on Machine Learning ICML 2024, Vienna, Austria.https://hdl.handle.net/10289/17008The 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.Attribution 4.0 International © 2024 The Authorshttp://creativecommons.org/licenses/by/4.0/Conference Contribution2640-3498