在线异常检测新方法，实时追踪数据流变化，效率远超同类。
Online Isolation Forest
- 基于流式数据设计，无需重训即可持续跟踪数据演化
- 在真实数据集上性能媲美离线顶尖方法，且速度更快
- 适合对响应速度要求高的场景，如网络安防、金融反欺诈
异常检测研究中多为离线方法，需反复访问内存中的数据，难以适应流式场景。现有在线方法也普遍依赖周期性重训以适应变化。本文提出 Online-iForest，一种专为流式环境设计的新方法，能无缝追踪随时间演化的数据生成过程。在真实数据集上的实验表明，该方法性能与现有在线方法相当，接近需周期重训的先进离线技术。尤为突出的是，Online-iForest 在效率上始终优于所有对比方法，是网络安全、欺诈检测和故障预警等需快速识别异常场景的理想选择。
原文摘要 · Abstract (English)
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.
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