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Anomaly Detection Based on Isolation Mechanisms: A Survey

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  • Published: 06 September 2025
  • Volume 22, pages 849–865, (2025)
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Machine Intelligence Research Aims and scope Submit manuscript
Anomaly Detection Based on Isolation Mechanisms: A Survey
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  • Yang Cao  ORCID: orcid.org/0000-0003-2184-44911,
  • Haolong Xiang  ORCID: orcid.org/0000-0003-4565-88292,
  • Hang Zhang  ORCID: orcid.org/0000-0003-0401-40663,4,
  • Ye Zhu  ORCID: orcid.org/0000-0003-4776-49321 &
  • …
  • Kai Ming Ting  ORCID: orcid.org/0000-0001-7892-61943,4 

  • 812 Accesses

  • 1 Citation

  • 1 Altmetric

  • Explore all metrics

Abstract

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.

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Acknowledgements

We appreciate the suggestions from Shuaibin Song, Zijing Wang and Zongyou Liu from Nanjing University, China, and Dr Xuyun Zhang from Macquarie University, Australia. Kai Ming Ting is supported by the National Natural Science Foundation of China (No. 62076120). This project is supported by the State Key Laboratory for Novel Software Technology at Nanjing University, China (No. KFKT2024A01). Open Access funding enabled and organized by CAUL and its Member Institutions.

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Authors and Affiliations

  1. Deakin Cyber Research and Innovation Centre, Deakin University, Burwood, VIC, 3125, Australia

    Yang Cao & Ye Zhu

  2. School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Haolong Xiang

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Hang Zhang & Kai Ming Ting

  4. School of Artificial Intelligence, Nanjing University, Nanjing, 210023, China

    Hang Zhang & Kai Ming Ting

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  1. Yang Cao
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Correspondence to Ye Zhu.

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Yang Cao received the B. Sc. degree in information technology from Monash University, Australia in 2020, the M. Sc. degree in data science and the Ph. D. degree in artificial intelligence from Deakin University, Australia in 2021 and 2025, respectively. He is currently a postdoctoral researcher at Great Bay University, China.

His research interests include clustering analysis, anomaly detection and their application in renewable energy.

Haolong Xiang received the Ph. D. degree in artificial intelligence in the School of Computing, Macquarie University, Australia in 2024. He is currently working in the School of Software of Nanjing University of Information Science and Technology, China.

His research interests include anomaly detection, data mining and machine learning.

Hang Zhang received the B. Sc. degree in computer science from Tongji University, China in 2020. Currently, he is a Ph. D. degree candidate in the School of Artificial Intelligence, Nanjing University, China. And he is a member of Learning and Mining from Data (LAMDA) Group, China.

His research interests include machine learning, data mining and topological data analysis.

Ye Zhu received the Ph. D. degree in artificial intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University, Australia in 2017. He is a senior lecturer at the School of Information Technology, Deakin University, Australia. He has published more than 50 papers in AI-related top international conferences or journals, including SIGKDD, AAAI, IJCAI, VLDB, AIJ, TKDE, PRJ, JAIR, ISJ and MLJ. He is on the program committee of SIGKDD, AAAI, IJCAI, PAKDD and ADMA. He has also secured several large research grants for multi-disciplinary research. He is an IEEE Senior Member.

His research interests include clustering analysis, anomaly detection, and their applications for pattern recognition and information retrieval.

Kai Ming Ting received the Ph. D. degree in computer science from the University of Sydney, Australia in 1996, and has worked at various universities in Australia and New Zealand, including the University of Waikato, Deakin University, Monash University, and Federation University. He is a professor at Nanjing University, China since 2020. He is the principal driver of isolation-based methods, and a key originator of isolation forest, isolation kernel, isolation distributional kernel and mass-based similarity. He has received research grants from the National Natural Science Foundation of China, the Australian Research Council, the US Air Force of Scientific Research (AFOSR/AOARD), Toyota InfoTechnology Center, and the Australian Institute of Sports.

His research interests include data-dependent similarity measures, anomaly detection, ensemble approaches, data mining, and machine learning.

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Cao, Y., Xiang, H., Zhang, H. et al. Anomaly Detection Based on Isolation Mechanisms: A Survey. Mach. Intell. Res. 22, 849–865 (2025). https://doi.org/10.1007/s11633-025-1554-4

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  • Received: 30 May 2024

  • Accepted: 06 March 2025

  • Published: 06 September 2025

  • Version of record: 06 September 2025

  • Issue date: October 2025

  • DOI: https://doi.org/10.1007/s11633-025-1554-4

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Keywords

  • Isolation forest
  • isolation kernel
  • anomaly detection
  • isolation-based methods
  • machine learning
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