Anomaly detection is a longstanding and active research area that has many
applications in domains such as finance, security, and manufacturing. However,
the efficiency and performance of anomaly detection algorithms are challenged
by the large-scale, high-dimensional, and heterogeneous data that are prevalent
in the era of big data. Isolation-based unsupervised anomaly detection is a
novel and effective approach for identifying anomalies in data. It relies on
the idea that anomalies are few and different from normal instances, and thus
can be easily isolated by random partitioning. Isolation-based methods have
several advantages over existing methods, such as low computational complexity,
low memory usage, high scalability, robustness to noise and irrelevant
features, and no need for prior knowledge or heavy parameter tuning. In this
survey, we review the state-of-the-art isolation-based anomaly detection
methods, including their data partitioning strategies, anomaly score functions,
and algorithmic details. We also discuss some extensions and applications of
isolation-based methods in different scenarios, such as detecting anomalies in
streaming data, time series, trajectory, and image datasets. Finally, we
identify some open challenges and future directions for isolation-based anomaly
detection research.

孤立基于方法的异常检测方法是一种新颖且有效的方法，可以通过随机分割轻松地隔离出数据中的异常值。这项综述调查了最先进的孤立基于异常检测方法，包括它们的数据分割策略、异常得分函数和算法细节。还介绍了孤立基于方法在不同场景下的一些扩展和应用，例如在流数据、时间序列、轨迹和图像数据集中检测异常。最后，我们指出了一些孤立基于异常检测研究的开放挑战和未来方向。