anomaly detection resources

Anomaly detection related books, papers, videos, and toolboxes

8401

1754

Python

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 @ ([email protected]).
Enjoy reading!

BTW, you may find my [GitHub] <https://github.com/yzhao062>_ and
[outlier detection papers] <https://scholar.google.com/citations?user=zoGDYsoAAAAJ&hl=en>_ useful,
especially PyOD library <https://github.com/yzhao062/pyod>_ and ADBench benchmark <https://github.com/Minqi824/ADBench>_.

1. Books & Tutorials & Benchmarks <#1-books--tutorials--benchmarks>_
- 1.1. Books <#11-books>_
- 1.2. Tutorials <#12-tutorials>_
- 1.3. Benchmarks <#13-benchmarks>_
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. Overview & Survey Papers <#41-overview--survey-papers>_
- 4.2. Key Algorithms <#42-key-algorithms>_
- 4.3. Graph & Network Outlier Detection <#43-graph--network-outlier-detection>_
- 4.4. Time Series Outlier Detection <#44-time-series-outlier-detection>_
- 4.5. Feature Selection in Outlier Detection <#45-feature-selection-in-outlier-detection>_
- 4.6. High-dimensional & Subspace Outliers <#46-high-dimensional--subspace-outliers>_
- 4.7. Outlier Ensembles <#47-outlier-ensembles>_
- 4.8. Outlier Detection in Evolving Data <#48-outlier-detection-in-evolving-data>_
- 4.9. Representation Learning in Outlier Detection <#49-representation-learning-in-outlier-detection>_
- 4.10. Interpretability <#410-interpretability>_
- 4.11. Outlier Detection with Neural Networks <#411-outlier-detection-with-neural-networks>_
- 4.12. Active Anomaly Detection <#412-active-anomaly-detection>_
- 4.13. Interactive Outlier Detection <#413-interactive-outlier-detection>_
- 4.14. Outlier Detection in Other fields <#414-outlier-detection-in-other-fields>_
- 4.15. Outlier Detection Applications <#415-outlier-detection-applications>_
- 4.16. Automated Outlier Detection <#416-automated-outlier-detection>_
- 4.17. Machine Learning Systems for Outlier Detection <#417-machine-learning-systems-for-outlier-detection>_
- 4.18. Fairness and Bias in Outlier Detection <#418-fairness-and-bias-in-outlier-detection>_
- 4.19. Isolation-based Methods <#419-isolation-based-methods>_
- 4.20. Emerging and Interesting Topics <#420-emerging-and-interesting-topics>_
5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>_
- 5.1. Conferences & Workshops <#51-conferences--workshops>_
- 5.2. Journals <#52-journals>_

Books & Tutorials & Benchmarks

1.1. Books
^^^^^^^^^^

Outlier Analysis <https://link.springer.com/book/10.1007/978-3-319-47578-3>_
by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques.
A must-read for people in the field of outlier detection. [Preview.pdf] <http://charuaggarwal.net/outlierbook.pdf>_

Outlier Ensembles: An Introduction <https://www.springer.com/gp/book/9783319547640>_
by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.

Data Mining: Concepts and Techniques (3rd) <https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1>_
by Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. [Google Search] <https://www.google.ca/search?&q=data+mining+jiawei+han&oq=data+ming+jiawei>_

1.2. Tutorials
^^^^^^^^^^^^^^

1.3. Benchmarks
^^^^^^^^^^^^^^^

News: We just released a 36-page, the most comprehensive anomaly detection benchmark paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-preprint-adbench.pdf>.
The fully open-sourced ADBench <https://github.com/Minqi824/ADBench> compares 30 anomaly detection algorithms on 55 benchmark datasets.

Courses/Seminars/Videos

Coursera Introduction to Anomaly Detection (by IBM)\ :
[See Video] <https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection>_

Get started with the Anomaly Detection API (by IBM)\ :
[See Website] <https://developer.ibm.com/learningpaths/get-started-anomaly-detection-api/>_

Practical Anomaly Detection by appliedAI Institute:
[See Website] <https://transferlab.ai/trainings/practical-anomaly-detection/>, [See Video] <https://www.youtube.com/watch?v=sEoMIDARpJ0&list=PLz6xKPm1Bnd6cDDgct3MDhNWJuPXzsmyW>, [See GitHub] <https://github.com/aai-institute/tfl-training-practical-anomaly-detection>_

Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic\ :
[See Video] <https://www.coursera.org/learn/real-time-cyber-threat-detection>_

Coursera Machine Learning by Andrew Ng also partly covers the topic\ :

Anomaly Detection vs. Supervised Learning <https://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning>_
Developing and Evaluating an Anomaly Detection System <https://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system>_

Udemy Outlier Detection Algorithms in Data Mining and Data Science\ :
[See Video] <https://www.udemy.com/outlier-detection-techniques/>_

Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques\ :
[See Video] <http://web.stanford.edu/class/cs259d/>_

Toolbox & Datasets

3.1. Multivariate Data
^^^^^^^^^^^^^^^^^^^^^^

[Python] Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>_\ : PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.

[Python, GPU] TOD: Tensor-based Outlier Detection (PyTOD) <https://github.com/yzhao062/pytod>_: A general GPU-accelerated framework for outlier detection.

[Python] Python Streaming Anomaly Detection (PySAD) <https://github.com/selimfirat/pysad>_\ : PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.

[Python] Scikit-learn Novelty and Outlier Detection <http://scikit-learn.org/stable/modules/outlier_detection.html>_. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.

[Python] Scalable Unsupervised Outlier Detection (SUOD) <https://github.com/yzhao062/suod>_\ : SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.

[Julia] OutlierDetection.jl <https://github.com/OutlierDetectionJL/OutlierDetection.jl>_\ : OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.

[Java] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures <https://elki-project.github.io/>_\ :
ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.

[Java] RapidMiner Anomaly Detection Extension <https://github.com/Markus-Go/rapidminer-anomalydetection>_\ : The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.

[R] CRAN Task View: Anomaly Detection with R <https://github.com/pridiltal/ctv-AnomalyDetection>_\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.

[R] outliers package <https://cran.r-project.org/web/packages/outliers/index.html>_\ : A collection of some tests commonly used for identifying outliers in R.

[Matlab] Anomaly Detection Toolbox - Beta <http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/>_\ : A collection of popular outlier detection algorithms in Matlab.

3.2. Time Series Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

[Python] TODS <https://github.com/datamllab/tods>_\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

[Python] skyline <https://github.com/earthgecko/skyline>_\ : Skyline is a near real time anomaly detection system.

[Python] banpei <https://github.com/tsurubee/banpei>_\ : Banpei is a Python package of the anomaly detection.

[Python] telemanom <https://github.com/khundman/telemanom>_\ : A framework for using LSTMs to detect anomalies in multivariate time series data.

[Python] DeepADoTS <https://github.com/KDD-OpenSource/DeepADoTS>_\ : A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.

[Python] NAB: The Numenta Anomaly Benchmark <https://github.com/numenta/NAB>_\ : NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.

[Python] CueObserve <https://github.com/cuebook/CueObserve>_\ : Anomaly detection on SQL data warehouses and databases.

[Python] Chaos Genius <https://github.com/chaos-genius/chaos_genius>_\ : ML powered analytics engine for outlier/anomaly detection and root cause analysis.

[R] CRAN Task View: Anomaly Detection with R <https://github.com/pridiltal/ctv-AnomalyDetection>_\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.

[R] AnomalyDetection <https://github.com/twitter/AnomalyDetection>_\ : AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.

[R] anomalize <https://cran.r-project.org/web/packages/anomalize/>_\ : The ‘anomalize’ package enables a “tidy” workflow for detecting anomalies in data.

3.3. Graph Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

[Python] Python Graph Outlier Detection (PyGOD) <https://github.com/pygod-team/pygod/>_\ : PyGOD is a Python library for graph outlier detection (anomaly detection). It includes more than 10 latest graph-based detection algorithms

3.4. Real-time Elasticsearch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

[Open Distro] Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon <https://github.com/aws/random-cut-forest-by-aws>\ : A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See Real Time Anomaly Detection in Open Distro for Elasticsearch <https://opendistro.github.io/for-elasticsearch/blog/odfe-updates/2019/11/real-time-anomaly-detection-in-open-distro-for-elasticsearch/>.

[Python] datastream.io <https://github.com/MentatInnovations/datastream.io>_\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.

3.5. Datasets
^^^^^^^^^^^^^

ELKI Outlier Datasets\ : https://elki-project.github.io/datasets/outlier

Outlier Detection DataSets (ODDS)\ : http://odds.cs.stonybrook.edu/#table1

Unsupervised Anomaly Detection Dataverse\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF

Anomaly Detection Meta-Analysis Benchmarks\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155

Skoltech Anomaly Benchmark (SKAB)\ : https://github.com/waico/skab

Papers

4.1. Overview & Survey Papers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Papers are sorted by the publication year.

====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
Paper Title Venue Year Ref Materials
====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
A survey of outlier detection methodologies ARTIF INTELL REV 2004 [#Hodge2004A]_ [PDF] <https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf>_
Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ [PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>_
A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ [PDF] <https://arxiv.org/pdf/1503.01158.pdf>_
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [#Campos2016On]_ [HTML] <https://link.springer.com/article/10.1007/s10618-015-0444-8>, [SLIDES] <https://imada.sdu.dk/~zimek/InvitedTalks/TUVienna-2016-05-18-outlier-evaluation.pdf>
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [#Goldstein2016A]_ [PDF] <http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable>_
A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [#Domingues2018A]_ [PDF] <https://www.researchgate.net/publication/320025854_A_comparative_evaluation_of_outlier_detection_algorithms_Experiments_and_analyses>_
Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ [HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>_
Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ [HTML] <https://dl.acm.org/citation.cfm?id=3297314>_
Progress in Outlier Detection Techniques: A Survey IEEE Access 2019 [#Wang2019Progress]_ [PDF] <https://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf>_
Deep learning for anomaly detection: A survey Preprint 2019 [#Chalapathy2019Deep]_ [PDF] <https://arxiv.org/pdf/1901.03407.pdf>_
Anomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [#Bulusu2020Deep]_ [PDF] <https://arxiv.org/pdf/2003.06979.pdf>_
Anomaly detection in univariate time-series: A survey on the state-of-the-art Preprint 2020 [#Braei2020Anomaly]_ [PDF] <https://arxiv.org/pdf/2004.00433.pdf>_
Deep Learning for Anomaly Detection: A Review CSUR 2021 [#Pang2020Deep]_ [PDF] <https://arxiv.org/pdf/2007.02500.pdf>_
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ [PDF] <https://arxiv.org/pdf/2106.07178.pdf>_
Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ [PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>, [Code] <https://github.com/datamllab/tods/tree/benchmark>
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges Preprint 2021 [#Salehi2021A]_ [PDF] <https://arxiv.org/pdf/2110.14051.pdf>_
Self-Supervised Anomaly Detection: A Survey and Outlook Preprint 2022 [#Hojjati2022Self]_ [PDF] <https://arxiv.org/pdf/2205.05173.pdf>_
Weakly supervised anomaly detection: A survey Preprint 2023 [#Jiang2023weakly]_ [PDF] <https://arxiv.org/abs/2302.04549>, [PDF] <https://github.com/yzhao062/wsad>
====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================

4.2. Key Algorithms
^^^^^^^^^^^^^^^^^^^

All these algorithms are available in Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>_.

4.3. Graph & Network Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.4. Time Series Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.5. Feature Selection in Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.6. High-dimensional & Subspace Outliers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.7. Outlier Ensembles
^^^^^^^^^^^^^^^^^^^^^^

4.8. Outlier Detection in Evolving Data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
Paper Title Venue Year Ref Materials
================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction] SIGKDD Explorations 2018 [#Salehi2018A]_ [PDF] <http://www.kdd.org/exploration_files/20-1-Article2.pdf>_
Unsupervised real-time anomaly detection for streaming data Neurocomputing 2017 [#Ahmad2017Unsupervised]_ [PDF] <https://www.researchgate.net/publication/317325599_Unsupervised_real-time_anomaly_detection_for_streaming_data>_
Outlier Detection in Feature-Evolving Data Streams SIGKDD 2018 [#Manzoor2018Outlier]_ [PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-kdd-xstream.pdf>, [Github] <https://cmuxstream.github.io/>
Evaluating Real-Time Anomaly Detection Algorithms–The Numenta Anomaly Benchmark ICMLA 2015 [#Lavin2015Evaluating]_ [PDF] <https://arxiv.org/pdf/1510.03336.pdf>, [Github] <https://github.com/numenta/NAB>
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams AAAI 2020 [#Bhatia2020MIDAS]_ [PDF] <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf>, [Github] <https://github.com/bhatiasiddharth/MIDAS>
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing VLDB 2019 [#Yoon2019NETS]_ [PDF] <http://www.vldb.org/pvldb/vol12/p1303-yoon.pdf>, [Github] <https://github.com/kaist-dmlab/NETS>, [Slide] <https://drive.google.com/file/d/1wqKJZhEE4nTWe0zODu21ejgPDsDA_xaF/view?usp=sharing>_
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping KDD 2020 [#Yoon2020STARE]_ [PDF] <https://dl.acm.org/doi/pdf/10.1145/3394486.3403171>, [Github] <https://github.com/kaist-dmlab/STARE>, [Slide] <https://drive.google.com/file/d/11y7Gs703SKJBkPZ4nKKgua__dHXXMbkV/view?usp=sharing>_
Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries SIGMOD 2021 [#Yoon2021MDUAL]_ [PDF] <https://dl.acm.org/doi/pdf/10.1145/3448016.3452810>, [Github] <https://github.com/kaist-dmlab/MDUAL>, [Slide] <https://drive.google.com/file/d/1wmkkKCAcF9Dk8Wg49WnJF4U--lbtWy9J/view>_
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream KDD 2022 [#Yoon2022ARCUS]_ [PDF] <https://dl.acm.org/doi/pdf/10.1145/3534678.3539348>, [Github] <https://github.com/kaist-dmlab/ARCUS>, [Slide] <https://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view>_
================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================

4.9. Representation Learning in Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.10. Interpretability
^^^^^^^^^^^^^^^^^^^^^^

4.11. Outlier Detection with Neural Networks
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.12. Active Anomaly Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.13. Interactive Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.14. Outlier Detection in Other fields
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.15. Outlier Detection Applications
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.16. Automated Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.17. Machine Learning Systems for Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This section summarizes a list of systems for outlier detection, which may
overlap with the section of tools and libraries.

4.18. Fairness and Bias in Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.19. Isolation-Based Methods
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

4.20. Emerging and Interesting Topics
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Key Conferences/Workshops/Journals

5.1. Conferences & Workshops
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Key data mining conference deadlines, historical acceptance rates, and more
can be found data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>_.

ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) <http://www.kdd.org/conferences>. Note: SIGKDD usually has an Outlier Detection Workshop (ODD), see ODD 2021 <https://oddworkshop.github.io/>.

ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>_

The Web Conference (WWW) <https://www2018.thewebconf.org/>_

IEEE International Conference on Data Mining (ICDM) <http://icdm2018.org/>_

SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>_

IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>_

ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>_

ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>_

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>_

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>_

5.2. Journals
^^^^^^^^^^^^^

ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>_

IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>_

ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>_

Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>_

Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>_

References

… [#Abe2006Outlier] Abe, N., Zadrozny, B. and Langford, J., 2006, August. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 504-509, ACM.

… [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter\ , 14(2), pp.49-58.

… [#Ahmed2016A] Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems\ , 55, pp.278-288.

… [#Ahmad2017Unsupervised] Ahmad, S., Lavin, A., Purdy, S. and Agha, Z., 2017. Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, pp.134-147.

… [#Akoglu2015Graph] Akoglu, L., Tong, H. and Koutra, D., 2015. Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery\ , 29(3), pp.626-688.

… [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In European Conference on Principles of Data Mining and Knowledge Discovery, pp. 15-27.

… [#Arnaldo2019ex2] Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA).

… [#Bahri2022automl] Bahri, M., Salutari, F., Putina, A. et al. AutoML: state of the art with a focus on anomaly detection, challenges, and research directions. International Journal of Data Science and Analytics (2022).

… [#Bandaragoda2018Isolation] Bandaragoda, Tharindu R., Kai Ming Ting, David Albrecht, Fei Tony Liu, Ye Zhu, and Jonathan R. Wells. “Isolation‐based anomaly detection using nearest‐neighbor ensembles.” Computational Intelligence 34, no. 4 (2018): 968-998.

… [#Bhatia2020MIDAS] Bhatia, S., Hooi, B., Yoon, M., Shin, K. and Faloutsos. C., 2020. MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. In AAAI Conference on Artificial Intelligence (AAAI).

… [#Braei2020Anomaly] Braei, M. and Wagner, S., 2020. Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv preprint arXiv:2004.00433.

… [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. ACM SIGMOD Record\ , 29(2), pp. 93-104.

… [#Bulusu2020Deep] Bulusu, S., Kailkhura, B., Li, B., Varshney, P. and Song, D., 2020. Anomalous instance detection in deep learning: A survey (No. LLNL-CONF-808677). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).

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anomaly detection resources

Anomaly Detection Learning Resources

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References