Anomaly Detection Learning Resources ==================================== .. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers :alt: GitHub stars .. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue :target: https://github.com/yzhao062/anomaly-detection-resources/network :alt: GitHub forks .. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE :alt: License .. image:: https://awesome.re/badge-flat2.svg :target: https://awesome.re/badge-flat2.svg :alt: Awesome .. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink :target: https://github.com/Minqi824/ADBench :alt: Benchmark ---- `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 @ (zhaoy@cmu.edu). 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. ---- Table of Contents ----------------- * `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>`_ ---- 1. Books & Tutorials & Benchmarks --------------------------------- 1.1. Books ^^^^^^^^^^ `Outlier Analysis <https://www.springer.com/gp/book/9781461463955>`_ 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 ^^^^^^^^^^^^^^ ===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== Tutorial Title Venue Year Ref Materials ===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== Data mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ `[Video] <http://videolectures.net/ecmlpkdd08_lazarevic_dmfa/>`_ Outlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ `[PDF] <https://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf>`_ Anomaly Detection: A Tutorial ICDM 2011 [#Chawla2011Anomaly]_ `[PDF] <http://webdocs.cs.ualberta.ca/~icdm2011/downloads/ICDM2011_anomaly_detection_tutorial.pdf>`_ Anomaly Detection in Networks KDD 2017 [#Mendiratta2017Anomaly]_ `[Page] <https://veena-mendiratta.blog/tutorial-anomaly-detection-in-networks/>`_ Which Outlier Detector Should I use? ICDM 2018 [#Ting2018Which]_ `[PDF] <https://ieeexplore.ieee.org/document/8594824>`_ Deep Learning for Anomaly Detection KDD 2020 [#Wang2020Deep]_ `[HTML] <https://sites.google.com/view/kdd2020deepeye/home>`_, `[Video] <https://www.youtube.com/watch?v=Fn0qDbKL3UI&list=PLn0nrSd4xjja7AD3aY9Jxmr820gx59EQC&index=66>`_ Deep Learning for Anomaly Detection WSDM 2021 [#Pang2021Deep]_ `[HTML] <https://sites.google.com/site/gspangsite/wsdm21_tutorial>`_ ===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== 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. ============= ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Data Types Paper Title Venue Year Ref Materials ============= ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Time-series 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>`_ Graph Benchmarking Node Outlier Detection on Graphs Preprint 2022 [#Liu2022Benchmarking]_ `[PDF] <https://arxiv.org/abs/2206.10071>`_, `[Code] <https://github.com/pygod-team/pygod/tree/main/benchmark>`_ Tabular ADBench: Anomaly Detection Benchmark Preprint 2022 [#Han2022Adbench]_ `[PDF] <https://arxiv.org/abs/2206.09426>`_, `[Code] <https://github.com/Minqi824/ADBench>`_ ============= ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== ---- 2. 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/>`_ **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/>`_ ---- 3. 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 ---- 4. 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>`_ ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== 4.2. Key Algorithms ^^^^^^^^^^^^^^^^^^^ All these algorithms are available in `Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>`_. ==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== Abbreviation Paper Title Venue Year Ref Materials ==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== kNN Efficient algorithms for mining outliers from large data sets ACM SIGMOD Record 2000 [#Ramaswamy2000Efficient]_ `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/pub/check/ramaswamy.pdf>`_ KNN Fast outlier detection in high dimensional spaces PKDD 2002 [#Angiulli2002Fast]_ `[PDF] <https://www.researchgate.net/profile/Clara_Pizzuti/publication/220699183_Fast_Outlier_Detection_in_High_Dimensional_Spaces/links/542ea6a60cf27e39fa9635c6.pdf>`_ LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] <http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_ IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_ OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_ AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_ COPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [#Li2020COPOD]_ `[PDF] <http://www.andrew.cmu.edu/user/yuezhao2/papers/20-icdm-copod.pdf>`_ ECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions TKDE 2022 [#Li2021ECOD]_ `[PDF] <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-tkde-ecod.pdf>`_ ==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== 4.3. Graph & Network Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== Graph based anomaly detection and description: a survey DMKD 2015 [#Akoglu2015Graph]_ `[PDF] <https://arxiv.org/pdf/1404.4679.pdf>`_ Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ `[PDF] <https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347>`_ Outlier detection in graphs: On the impact of multiple graph models ComSIS 2019 [#Campos2019Outlier]_ `[PDF] <http://www.comsis.org/pdf.php?id=wims-8671>`_ A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_ ================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== 4.4. Time Series Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_ Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_ Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] <https://arxiv.org/pdf/1906.03821.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>`_ Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series ICLR 2022 [#Dai2022Graph]_ `[PDF] <https://openreview.net/pdf?id=45L_dgP48Vd>`_, `[Code] <https://github.com/EnyanDai/GANF>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.5. Feature Selection in Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings ICDM 2016 [#Pang2016Unsupervised]_ `[PDF] <https://opus.lib.uts.edu.au/bitstream/10453/107356/4/DSFS_ICDM2016.pdf>`_ Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection IJCAI 2017 [#Pang2017Learning]_ `[PDF] <https://www.ijcai.org/proceedings/2017/0360.pdf>`_ ================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== 4.6. High-dimensional & Subspace Outliers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= A survey on unsupervised outlier detection in high-dimensional numerical data Stat Anal Data Min 2012 [#Zimek2012A]_ `[HTML] <https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11161>`_ Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_ Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 [#Radovanovic2015Reverse]_ `[PDF] <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.699.9559&rep=rep1&type=pdf>`_, `[SLIDES] <https://pdfs.semanticscholar.org/c8aa/832362422418287ff56793c780b425afa93f.pdf>`_ Outlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ `[PDF] <http://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf>`_ ================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= 4.7. Outlier Ensembles ^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Outlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ `[PDF] <https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf>`_ Ensembles for unsupervised outlier detection: challenges and research questions a position paper SIGKDD Explorations 2014 [#Zimek2014Ensembles]_ `[PDF] <http://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf>`_ An Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 [#Campos2018An]_ `[HTML] <https://link.springer.com/chapter/10.1007/978-3-319-93034-3_45>`_ LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66>`_ 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>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.8. 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 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_ Learning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_ XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] <http://www.andrew.cmu.edu/user/yuezhao2/papers/18-ijcnn-xgbod.pdf>`_ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== 4.10. Interpretability ^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Explaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 [#Macha2018Explaining]_ `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-journal-xpacs.pdf>`_ Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf>`_ Contextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ `[PDF] <https://arxiv.org/pdf/1711.10589.pdf>`_ Mining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ `[PDF] <http://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf>`_ Discriminative features for identifying and interpreting outliers ICDE 2014 [#Dang2014Discriminative]_ `[PDF] <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.706.5744&rep=rep1&type=pdf>`_ Sequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3230666>`_ Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network WWW 2021 [#Xu2021Beyond]_ `[PDF] <https://jiansonglei.github.io/files/21WWW.pdf>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.11. Outlier Detection with Neural Networks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_ MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks ICANN 2019 [#Li2019MAD]_ `[PDF] <https://arxiv.org/pdf/1901.04997.pdf>`_, `[Code] <https://github.com/LiDan456/MAD-GANs>`_ Generative Adversarial Active Learning for Unsupervised Outlier Detection TKDE 2019 [#Liu2019Generative]_ `[PDF] <https://arxiv.org/pdf/1809.10816.pdf>`_, `[Code] <https://github.com/leibinghe/GAAL-based-outlier-detection>`_ Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 [#Zong2018Deep]_ `[PDF] <http://www.cs.ucsb.edu/~bzong/doc/iclr18-dagmm.pdf>`_, `[Code] <https://github.com/danieltan07/dagmm>`_ Deep Anomaly Detection with Outlier Exposure ICLR 2019 [#Hendrycks2019Deep]_ `[PDF] <https://arxiv.org/pdf/1812.04606.pdf>`_, `[Code] <https://github.com/hendrycks/outlier-exposure>`_ Unsupervised Anomaly Detection With LSTM Neural Networks TNNLS 2019 [#Ergen2019Unsupervised]_ `[PDF] <https://arxiv.org/pdf/1710.09207.pdf>`_, `[IEEE] <https://ieeexplore.ieee.org/document/8836638>`_, Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network NeurIPS 2019 [#Wang2019Effective]_ `[PDF] <https://papers.nips.cc/paper/8830-effective-end-to-end-unsupervised-outlier-detection-via-inlier-priority-of-discriminative-network.pdf>`_ `[Code] <https://github.com/demonzyj56/E3Outlier>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.12. Active Anomaly Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Active learning for anomaly and rare-category detection NeurIPS 2005 [#Pelleg2005Active]_ `[PDF] <http://papers.nips.cc/paper/2554-active-learning-for-anomaly-and-rare-category-detection.pdf>`_ Outlier detection by active learning SIGKDD 2006 [#Abe2006Outlier]_ `[PDF] <https://www.researchgate.net/profile/Naoki_Abe2/publication/221653343_Outlier_detection_by_active_learning/links/5441464a0cf2e6f0c0f60abb.pdf>`_ Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_ Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [#Zha2020Meta]_ `[PDF] <https://arxiv.org/pdf/2009.07415.pdf>`_ A3: Activation Anomaly Analysis ECML-PKDD 2020 [#Sperl2021A3]_ `[PDF] <https://arxiv.org/pdf/2003.01801>`_, `[Code] <https://github.com/Fraunhofer-AISEC/A3>`_ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== 4.13. Interactive Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback SDM 2019 [#Lamba2019Learning]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69>`_ Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_ eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_ Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.14. Outlier Detection in Other fields ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Field Paper Title Venue Year Ref Materials ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== **Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_ ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.15. Outlier Detection Applications ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Field Paper Title Venue Year Ref Materials ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== **Security** A survey of distance and similarity measures used within network intrusion anomaly detection IEEE Commun. Surv. Tutor. 2015 [#WellerFahy2015A]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6853338>`_ **Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] <http://dtstc.ugr.es/~jedv/descargas/2009_CoSe09-Anomaly-based-network-intrusion-detection-Techniques,-systems-and-challenges.pdf>`_ **Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] <http://isiarticles.com/bundles/Article/pre/pdf/76882.pdf>`_ **Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_ **Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_ **Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_ **Machine Failure** Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [#Riazi2019Detecting]_ `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/postscript/DAWAK19.pdf>`_ **Video Surveillance** AnomalyNet: An anomaly detection network for video surveillance TIFS 2019 [#Zhou2019AnomalyNet]_ `[IEEE] <https://ieeexplore.ieee.org/document/8649753>`_, `Code <https://github.com/joeyzhouty/AnomalyNet>`_ ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.16. Automated Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== AutoML: state of the art with a focus on anomaly detection, challenges, and research directions Int J Data Sci Anal 2022 [#Bahri2022automl]_ `[PDF] <https://www.researchgate.net/publication/358364044_AutoML_state_of_the_art_with_a_focus_on_anomaly_detection_challenges_and_research_directions>`_ AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning ICDE 2020 [#Li2020AutoOD]_ `[PDF] <https://arxiv.org/pdf/2006.11321.pdf>`_ Automatic Unsupervised Outlier Model Selection NeurIPS 2021 [#Zhao2020Automating]_ `[PDF] <https://www.andrew.cmu.edu/user/yuezhao2/papers/21-neurips-metaod.pdf>`_, `[Code] <https://github.com/yzhao062/MetaOD>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 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. ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== PyOD: A Python Toolbox for Scalable Outlier Detection JMLR 2019 [#Zhao2019PYOD]_ `[PDF] <https://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_, `[Code] <https://github.com/yzhao062/pyod>`_ SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection MLSys 2021 [#Zhao2021SUOD]_ `[PDF] <https://arxiv.org/pdf/2003.05731.pdf>`_, `[Code] <https://github.com/yzhao062/suod>`_ TOD: Tensor-based Outlier Detection Preprint 2021 [#Zhao2021TOD]_ `[PDF] <https://arxiv.org/pdf/2110.14007.pdf>`_, `[Code] <https://github.com/yzhao062/pytod>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.18. Fairness and Bias in Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== A Framework for Determining the Fairness of Outlier Detection ECAI 2020 [#Davidson2020A]_ `[PDF] <https://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf>`_ FAIROD: Fairness-aware Outlier Detection AIES 2021 [#Shekhar2021FAIROD]_ `[PDF] <https://arxiv.org/pdf/2012.03063.pdf>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.19. Isolation-Based Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_ Isolation‐based anomaly detection using nearest‐neighbor ensembles Computational Intelligence 2018 [#Bandaragoda2018Isolation]_ `[PDF] <https://www.researchgate.net/publication/322359651_Isolation-based_anomaly_detection_using_nearest-neighbor_ensembles_iNNE>`_, `[Code] <https://github.com/zhuye88/iNNE>`_ Extended Isolation Forest TKDE 2019 [#Hariri2019Extended]_ `[PDF] <https://arxiv.org/pdf/1811.02141.pdf>`_, `[Code] <https://github.com/sahandha/eif>`_ Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection KDD 2020 [#Ting2020Isolation]_ `[PDF] <https://arxiv.org/pdf/2009.12196.pdf>`_, `[Code] <https://github.com/IsolationKernel/Codes/tree/main/IDK>`_ ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== 4.20. Emerging and Interesting Topics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Clustering with Outlier Removal TKDE 2019 [#Liu2018Clustering]_ `[PDF] <https://arxiv.org/pdf/1801.01899.pdf>`_ Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning IEEE Trans. Ind. Informat. 2020 [#Castellani2020Siamese]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9179030>`_ SSD: A Unified Framework for Self-Supervised Outlier Detection ICLR 2021 [#Sehwag2021SSD]_ `[PDF] <https://openreview.net/pdf?id=v5gjXpmR8J>`_, `[Code] <https://github.com/inspire-group/SSD>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== ---- 5. 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). .. [#Campos2016On] Campos, G.O., Zimek, A., Sander, J., Campello, R.J., Micenková, B., Schubert, E., Assent, I. and Houle, M.E., 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. *Data Mining and Knowledge Discovery*\ , 30(4), pp.891-927. .. [#Campos2018An] Campos, G.O., Zimek, A. and Meira, W., 2018, June. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In *Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 564-576)*. Springer, Cham. .. [#Campos2019Outlier] Campos, G.O., Moreira, E., Meira Jr, W. and Zimek, A., 2019. Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. *Computer Science & Information Systems*, 16(2). .. [#Castellani2020Siamese] Castellani, A., Schmitt, S., Squartini, S., 2020. Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning. In *IEEE Transactions on Industrial Informatics*. .. [#Chalapathy2019Deep] Chalapathy, R. and Chawla, S., 2019. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. .. [#Chandola2009Anomaly] Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. *ACM computing surveys* , 41(3), p.15. .. [#Chawla2011Anomaly] Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. *Tutorial at ICDM 2011*. .. [#Chen2017Outlier] Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. *SIAM International Conference on Data Mining*, pp. 90-98. Society for Industrial and Applied Mathematics. .. [#Dai2022Graph] Dai, E. and Chen, J., 2022. Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series. 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