Anomaly Detection Learning Resources

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Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>_ (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.

This repository collects:

#. Books & Academic Papers #. Online Courses and Videos #. Outlier Datasets #. Open-source and Commercial Libraries/Toolkits #. Key Conferences & Journals

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (yzhao010@usc.edu). Enjoy reading!

BTW, you may find my [GitHub] <https://github.com/yzhao062>, [USC FORTIS Lab] <https://github.com/USC-FORTIS>, and [Google Scholar] <https://scholar.google.com/citations?user=zoGDYsoAAAAJ&hl=en>_ relevant, especially PyOD library <https://github.com/yzhao062/pyod>, ADBench benchmark <https://github.com/Minqi824/ADBench>, and NLP-ADBench: NLP Anomaly Detection Benchmark <https://github.com/USC-FORTIS/NLP-ADBench>_,.

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Table of Contents

• 1. Books & Tutorials & Benchmarks <#1-books--tutorials--benchmarks>_

  • 1.1. Benchmarks <#13-benchmarks>_
  • 1.2. Tutorials <#12-tutorials>_
  • 1.3. Books <#11-books>_

• 2. Courses/Seminars/Videos <#2-coursesseminarsvideos>_

• 3. Toolbox & Datasets <#3-toolbox--datasets>_

  • 3.1. Multivariate data outlier detection <#31-multivariate-data>_
  • 3.2. Time series outlier detection <#32-time-series-outlier-detection>_
  • 3.3. Graph Outlier Detection <#33-graph-outlier-detection>_
  • 3.4. Real-time Elasticsearch <#34-real-time-elasticsearch>_
  • 3.5. Datasets <#35-datasets>_

• 4. Papers <#4-papers>_

  • 4.1. LLM and LLM Agents for Anomaly Detection <#41-llm-and-llm-agents-for-anomaly-detection>_
  • 4.2. Emerging and Interesting Topics <#42-emerging-and-interesting-topics>_
  • 4.3. Weakly-supervised Methods <#43-weakly-supervised-methods>_
  • 4.4. Machine Learning Systems for Outlier Detection <#44-machine-learning-systems-for-outlier-detection>_
  • 4.5. Automated Outlier Detection <#45-automated-outlier-detection>_
  • 4.6. Outlier Detection with Neural Networks <#46-outlier-detection-with-neural-networks>_
  • 4.7. Interpretability <#47-interpretability>_
  • 4.8. Representation Learning in Outlier Detection <#48-representation-learning-in-outlier-detection>_
  • 4.9. Outlier Detection in Evolving Data <#49-outlier-detection-in-evolving-data>_
  • 4.10. Outlier Ensembles <#410-outlier-ensembles>_
  • 4.11. High-dimensional & Subspace Outliers <#411-high-dimensional--subspace-outliers>_
  • 4.12. Feature Selection in Outlier Detection <#412-feature-selection-in-outlier-detection>_
  • 4.13. Time Series Outlier Detection <#413-time-series-outlier-detection>_
  • `4.14. Graph & Network Outlier