Liu, Justin JiaCassales, GuilhermeLiu, Fei T.Pfahringer, BernhardBifet, Albert2026-10-062026-10-062025LIU, J. J., Weigert Cassales, G., Liu, F. T., Pfahringer, B., & Bifet, A. (2025). Streaming Isolation Forest. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15870 LNCS, 95-107. https://doi.org/10.1007/978-981-96-8170-9_8978-981-96-8170-9https://hdl.handle.net/10289/18690Anomaly detection is crucial to identify unusual patterns in various domains. In particular, continuous and rapid flow creates distinct challenges within streaming data. This paper introduces the Streaming Isolation Forest (SiForest), a novel algorithm that uses isolation principles and reservoir sampling to align the model with current data distributions. SiForest efficiently detects anomalies with minimal computational and memory requirements and dynamically updates its model using a subtree regrowing strategy. Empirical evaluation on twenty-three benchmark datasets demonstrates that SiForest outperforms eight state-of-the-art algorithms in terms of AUC-ROC scores, achieving greater precision and adaptability.enThis is an author’s accepted version of a conference paper published in the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2025). © 2025 Springer.anomaly detectioncomputer sciencedata streamsensemblesConference Contribution10.1007/978-981-96-8170-9_846 Information and Computing Sciences4611 Machine Learning46 Information and computing sciences