Anomaly Detection Based on Isolation Mechanisms: A Survey
Yang Cao Haolong Xiang Hang Zhang Ye Zhu Kai Ming Ting
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.