# Anomaly Detection Based on Isolation Mechanisms: A Survey

arXiv: https://arxiv.org/abs/2403.10802 · HTML: https://mlsanity.com/paper/2403.10802/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 112 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 28 of 28)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection](https://mlsanity.com/paper/2207.03579/) (2022)
- [Detecting semantic anomalies](https://mlsanity.com/paper/1908.04388/) (2019)
- [Autoencoders for unsupervised anomaly detection in high energy physics](https://mlsanity.com/paper/2104.09051/) (2021)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review](https://mlsanity.com/paper/2010.14946/) (2020)
- [Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees](https://mlsanity.com/paper/1605.09131/) (2016)
- [Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering](https://mlsanity.com/paper/1907.00378/) (2019)
- [Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion Detection](https://mlsanity.com/paper/2006.16801/) (2020)
- [Elastic bands across the path: A new framework and methods to lower bound DTW](https://mlsanity.com/paper/1808.09617/) (2018)
- [Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection](https://mlsanity.com/paper/2009.12196/) (2020)
- [Point-Set Kernel Clustering](https://mlsanity.com/paper/2002.05815/) (2020)
- [Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest](https://mlsanity.com/paper/2003.09365/) (2020)
- [Detecting Change Intervals with Isolation Distributional Kernel](https://mlsanity.com/paper/2212.14630/) (2022)
- [Extending Isolation Forest for Anomaly Detection in Big Data via K-Means](https://mlsanity.com/paper/2104.13190/) (2021)
- [OptIForest: Optimal Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2306.12703/) (2023)
- [Dynamic Time Warping and Geometric Edit Distance: Breaking the Quadratic Barrier](https://mlsanity.com/paper/1607.05994/) (2016)
- [Improved Anomaly Detection by Using the Attention-Based Isolation Forest](https://mlsanity.com/paper/2210.02558/) (2022)
- [Distribution-Based Trajectory Clustering](https://mlsanity.com/paper/2310.05123/) (2023)

## Built on by (most cited first, 10 of 10)

- [Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models](https://mlsanity.com/paper/2405.20348/) (2024)
- [Text Anomaly Detection with Simplified Isolation Kernel](https://mlsanity.com/paper/2510.13197/) (2025)
- [Towards Token-Level Text Anomaly Detection](https://mlsanity.com/paper/2601.13644/) (2026)
- [Real-Time Machine Learning for Embedded Anomaly Detection](https://mlsanity.com/paper/2512.19383/) (2025)
- [Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles](https://mlsanity.com/paper/2506.13828/) (2025)
- [Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study](https://mlsanity.com/paper/2507.05730/) (2025)
- [Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection](https://mlsanity.com/paper/2510.13311/) (2025)
- [Stochastic Voronoi Ensembles for Anomaly Detection](https://mlsanity.com/paper/2601.03664/) (2026)
- [SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection](https://mlsanity.com/paper/2609.08200/) (2026)
- [DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detection](https://mlsanity.com/paper/2609.22136/) (2026)

## Abstract (arXiv)

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.