├── LICENSE
├── README.rst
├── README_CN.rst
├── download.py
├── resource_urls
└── papers.txt
└── url_checker.py
/LICENSE:
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1 | GNU AFFERO GENERAL PUBLIC LICENSE
2 | Version 3, 19 November 2007
3 |
4 | Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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621 | How to Apply These Terms to Your New Programs
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623 | If you develop a new program, and you want it to be of the greatest
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662 |
--------------------------------------------------------------------------------
/README.rst:
--------------------------------------------------------------------------------
1 | Anomaly Detection Learning Resources
2 | ====================================
3 |
4 | .. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg
5 | :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers
6 | :alt: GitHub stars
7 |
8 |
9 | .. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue
10 | :target: https://github.com/yzhao062/anomaly-detection-resources/network
11 | :alt: GitHub forks
12 |
13 |
14 | .. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue
15 | :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE
16 | :alt: License
17 |
18 |
19 | .. image:: https://awesome.re/badge-flat2.svg
20 | :target: https://awesome.re/badge-flat2.svg
21 | :alt: Awesome
22 |
23 |
24 | .. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink
25 | :target: https://github.com/Minqi824/ADBench
26 | :alt: Benchmark
27 |
28 |
29 | ----
30 |
31 | `Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
32 | (also known as *Anomaly Detection*) is an exciting yet challenging field,
33 | which aims to identify outlying objects that are deviant from the general data distribution.
34 | Outlier detection has been proven critical in many fields, such as credit card
35 | fraud analytics, network intrusion detection, and mechanical unit defect detection.
36 |
37 | **This repository collects**:
38 |
39 |
40 | #. Books & Academic Papers
41 | #. Online Courses and Videos
42 | #. Outlier Datasets
43 | #. Open-source and Commercial Libraries/Toolkits
44 | #. Key Conferences & Journals
45 |
46 |
47 | **More items will be added to the repository**.
48 | Please feel free to suggest other key resources by opening an issue report,
49 | submitting a pull request, or dropping me an email @ (yzhao010@usc.edu).
50 | Enjoy reading!
51 |
52 | BTW, you may find my `[GitHub] <https://github.com/yzhao062>`_ and
53 | `[outlier detection papers] <https://scholar.google.com/citations?user=zoGDYsoAAAAJ&hl=en>`_ useful,
54 | especially `PyOD library <https://github.com/yzhao062/pyod>`_, `ADBench benchmark <https://github.com/Minqi824/ADBench>`_,
55 | and `AD-LLM: benchmarking LLMs for AD <https://github.com/USC-FORTIS/AD-LLM>`_.
56 |
57 | ----
58 |
59 | Table of Contents
60 | -----------------
61 |
62 |
63 | * `1. Books & Tutorials & Benchmarks <#1-books--tutorials--benchmarks>`_
64 |
65 | * `1.1. Books <#11-books>`_
66 | * `1.2. Tutorials <#12-tutorials>`_
67 | * `1.3. Benchmarks <#13-benchmarks>`_
68 |
69 | * `2. Courses/Seminars/Videos <#2-coursesseminarsvideos>`_
70 | * `3. Toolbox & Datasets <#3-toolbox--datasets>`_
71 |
72 | * `3.1. Multivariate data outlier detection <#31-multivariate-data>`_
73 | * `3.2. Time series outlier detection <#32-time-series-outlier-detection>`_
74 | * `3.3. Graph Outlier Detection <#33-graph-outlier-detection>`_
75 | * `3.4. Real-time Elasticsearch <#34-real-time-elasticsearch>`_
76 | * `3.5. Datasets <#35-datasets>`_
77 |
78 | * `4. Papers <#4-papers>`_
79 |
80 | * `4.1. Overview & Survey Papers <#41-overview--survey-papers>`_
81 | * `4.2. Key Algorithms <#42-key-algorithms>`_
82 | * `4.3. Graph & Network Outlier Detection <#43-graph--network-outlier-detection>`_
83 | * `4.4. Time Series Outlier Detection <#44-time-series-outlier-detection>`_
84 | * `4.5. Feature Selection in Outlier Detection <#45-feature-selection-in-outlier-detection>`_
85 | * `4.6. High-dimensional & Subspace Outliers <#46-high-dimensional--subspace-outliers>`_
86 | * `4.7. Outlier Ensembles <#47-outlier-ensembles>`_
87 | * `4.8. Outlier Detection in Evolving Data <#48-outlier-detection-in-evolving-data>`_
88 | * `4.9. Representation Learning in Outlier Detection <#49-representation-learning-in-outlier-detection>`_
89 | * `4.10. Interpretability <#410-interpretability>`_
90 | * `4.11. Outlier Detection with Neural Networks <#411-outlier-detection-with-neural-networks>`_
91 | * `4.12. Active Anomaly Detection <#412-active-anomaly-detection>`_
92 | * `4.13. Interactive Outlier Detection <#413-interactive-outlier-detection>`_
93 | * `4.14. Outlier Detection in Other fields <#414-outlier-detection-in-other-fields>`_
94 | * `4.15. Outlier Detection Applications <#415-outlier-detection-applications>`_
95 | * `4.16. Automated Outlier Detection <#416-automated-outlier-detection>`_
96 | * `4.17. Machine Learning Systems for Outlier Detection <#417-machine-learning-systems-for-outlier-detection>`_
97 | * `4.18. Fairness and Bias in Outlier Detection <#418-fairness-and-bias-in-outlier-detection>`_
98 | * `4.19. Isolation-based Methods <#419-isolation-based-methods>`_
99 | * `4.20. Weakly-supervised Methods <#420-weakly-supervised-methods>`_
100 | * `4.21. Emerging and Interesting Topics <#420-emerging-and-interesting-topics>`_
101 |
102 | * `5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>`_
103 |
104 | * `5.1. Conferences & Workshops <#51-conferences--workshops>`_
105 | * `5.2. Journals <#52-journals>`_
106 |
107 |
108 | ----
109 |
110 |
111 | 1. Books & Tutorials & Benchmarks
112 | ---------------------------------
113 |
114 | 1.1. Books
115 | ^^^^^^^^^^
116 |
117 | `Outlier Analysis <https://link.springer.com/book/10.1007/978-3-319-47578-3>`_
118 | by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques.
119 | A **must-read** for people in the field of outlier detection. `[Preview.pdf] <http://charuaggarwal.net/outlierbook.pdf>`_
120 |
121 | `Outlier Ensembles: An Introduction <https://www.springer.com/gp/book/9783319547640>`_
122 | by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.
123 |
124 | `Data Mining: Concepts and Techniques (3rd) <https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1>`_
125 | 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>`_
126 |
127 | 1.2. Tutorials
128 | ^^^^^^^^^^^^^^
129 |
130 | ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================
131 | Tutorial Title Venue Year Ref Materials
132 | ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================
133 | Data mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ `[Video] <http://videolectures.net/ecmlpkdd08_lazarevic_dmfa/>`_
134 | Outlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ `[PDF] <https://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf>`_
135 | Which Outlier Detector Should I use? ICDM 2018 [#Ting2018Which]_ `[PDF] <https://ieeexplore.ieee.org/document/8594824>`_
136 | 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>`_
137 | Deep Learning for Anomaly Detection WSDM 2021 [#Pang2021Deep]_ `[HTML] <https://sites.google.com/site/gspangsite/wsdm21_tutorial>`_
138 | Toward Explainable Deep Anomaly Detection KDD 2021 [#Pang2021Toward]_ `[HTML] <https://sites.google.com/site/gspangsite/kdd21_tutorial>`_
139 | Recent Advances in Anomaly Detection CVPR 2023 [#Pang2023recent]_ `[HTML] <https://sites.google.com/view/cvpr2023-tutorial-on-ad/>`_, `[Video] <https://www.youtube.com/watch?v=dXxrzWeybBo&feature=youtu.be>`_
140 | Trustworthy Anomaly Detection SDM 2024 [#Yuan2024Trustworthy]_ `[HTML] <https://yuan.shuhan.org/talks/SDM24/>`_
141 | ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================
142 |
143 |
144 | 1.3. Benchmarks
145 | ^^^^^^^^^^^^^^^
146 |
147 | **News**: We have two new works on NLP-based and LLM-based anomaly detection:
148 |
149 | - NLP-ADBench: NLP Anomaly Detection Benchmark
150 | - AD-LLM: Benchmarking Large Language Models for Anomaly Detection
151 |
152 | ============= ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
153 | Data Types Paper Title Venue Year Ref Materials
154 | ============= ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
155 | 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>`_
156 | Graph Benchmarking Node Outlier Detection on Graphs NeurIPS 2022 [#Liu2022Benchmarking]_ `[PDF] <https://arxiv.org/abs/2206.10071>`_, `[Code] <https://github.com/pygod-team/pygod/tree/main/benchmark>`_
157 | Graph GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection NeurIPS 2023 [#Tang2023GADBench]_ `[PDF] <https://arxiv.org/abs/2306.12251>`_, `[Code] <https://github.com/squareRoot3/GADBench>`_
158 | Tabular ADBench: Anomaly Detection Benchmark NeurIPS 2022 [#Han2022Adbench]_ `[PDF] <https://arxiv.org/abs/2206.09426>`_, `[Code] <https://github.com/Minqi824/ADBench>`_
159 | Tabular ADGym: Design Choices for Deep Anomaly Detection NeurIPS 2023 [#Jiang2023adgym]_ `[PDF] <https://arxiv.org/abs/2309.15376>`_, `[Code] <https://github.com/Minqi824/ADGym>`_
160 | NLP NLP-ADBench: NLP Anomaly Detection Benchmark Preprint 2024 [#Li2024NLPADBench]_ `[PDF] <https://arxiv.org/abs/2412.04784>`_, `[Code] <https://github.com/USC-FORTIS/NLP-ADBench>`_
161 | NLP AD-LLM: Benchmarking Large Language Models for Anomaly Detection Preprint 2024 [#Yang2024ADLLM]_ `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_
162 | ============= ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
163 |
164 |
165 | ----
166 |
167 | 2. Courses/Seminars/Videos
168 | --------------------------
169 |
170 | **Coursera Introduction to Anomaly Detection (by IBM)**\ :
171 | `[See Video] <https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection>`_
172 |
173 | **Get started with the Anomaly Detection API (by IBM)**\ :
174 | `[See Website] <https://developer.ibm.com/learningpaths/get-started-anomaly-detection-api/>`_
175 |
176 | **Practical Anomaly Detection by appliedAI Institute**\:
177 | `[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>`_
178 |
179 | **Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\ :
180 | `[See Video] <https://www.coursera.org/learn/real-time-cyber-threat-detection>`_
181 |
182 | **Coursera Machine Learning by Andrew Ng also partly covers the topic**\ :
183 |
184 |
185 | * `Anomaly Detection vs. Supervised Learning <https://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning>`_
186 | * `Developing and Evaluating an Anomaly Detection System <https://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system>`_
187 |
188 | **Udemy Outlier Detection Algorithms in Data Mining and Data Science**\ :
189 | `[See Video] <https://www.udemy.com/outlier-detection-techniques/>`_
190 |
191 | **Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\ :
192 | `[See Video] <http://web.stanford.edu/class/cs259d/>`_
193 |
194 | ----
195 |
196 | 3. Toolbox & Datasets
197 | ---------------------
198 |
199 | [**Python**] `OpenAD <https://github.com/USC-FORTIS/OpenAD>`_: OpenAD is a multi-agent framework designed to automate anomaly detection across diverse data modalities, including tabular, graph, time series, and more. It integrates modular agents, model selection strategies, and configurable pipelines to support extensible and interpretable detection workflows. The framework is under active development and aims to support both academic research and practical deployment.
200 |
201 | 3.1. Multivariate Data
202 | ^^^^^^^^^^^^^^^^^^^^^^
203 |
204 | [**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.
205 |
206 | [**Python**, **GPU**] `TOD: Tensor-based Outlier Detection (PyTOD) <https://github.com/yzhao062/pytod>`_: A general GPU-accelerated framework for outlier detection.
207 |
208 | [**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.
209 |
210 | [**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.
211 |
212 | [**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.
213 |
214 | [**Julia**] `OutlierDetection.jl <https://github.com/OutlierDetectionJL/OutlierDetection.jl>`_\ : OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.
215 |
216 | [**Java**] `ELKI: Environment for Developing KDD-Applications Supported by Index-Structures <https://elki-project.github.io/>`_\ :
217 | 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.
218 |
219 | [**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.
220 |
221 | [**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.
222 |
223 | [**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.
224 |
225 | [**Matlab**] `Anomaly Detection Toolbox - Beta <http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/>`_\ : A collection of popular outlier detection algorithms in Matlab.
226 |
227 |
228 | 3.2. Time Series Outlier Detection
229 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
230 |
231 | [**Python**] `TODS <https://github.com/datamllab/tods>`_\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
232 |
233 | [**Python**] `skyline <https://github.com/earthgecko/skyline>`_\ : Skyline is a near real time anomaly detection system.
234 |
235 | [**Python**] `banpei <https://github.com/tsurubee/banpei>`_\ : Banpei is a Python package of the anomaly detection.
236 |
237 | [**Python**] `telemanom <https://github.com/khundman/telemanom>`_\ : A framework for using LSTMs to detect anomalies in multivariate time series data.
238 |
239 | [**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.
240 |
241 | [**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.
242 |
243 | [**Python**] `CueObserve <https://github.com/cuebook/CueObserve>`_\ : Anomaly detection on SQL data warehouses and databases.
244 |
245 | [**Python**] `Chaos Genius <https://github.com/chaos-genius/chaos_genius>`_\ : ML powered analytics engine for outlier/anomaly detection and root cause analysis.
246 |
247 | [**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.
248 |
249 | [**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.
250 |
251 | [**R**] `anomalize <https://cran.r-project.org/web/packages/anomalize/>`_\ : The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
252 |
253 |
254 | 3.3. Graph Outlier Detection
255 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
256 |
257 | [**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
258 |
259 |
260 | 3.4. Real-time Elasticsearch
261 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
262 |
263 | [**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/>`_.
264 |
265 | [**Python**] `datastream.io <https://github.com/MentatInnovations/datastream.io>`_\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.
266 |
267 |
268 | 3.5. Datasets
269 | ^^^^^^^^^^^^^
270 |
271 | **NLP-ADBench**: NLP Anomaly Detection Benchmark and Datasets: https://github.com/USC-FORTIS/NLP-ADBench
272 |
273 | **ELKI Outlier Datasets**\ : https://elki-project.github.io/datasets/outlier
274 |
275 | **Outlier Detection DataSets (ODDS)**\ : http://odds.cs.stonybrook.edu/#table1
276 |
277 | **Unsupervised Anomaly Detection Dataverse**\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF
278 |
279 | **Anomaly Detection Meta-Analysis Benchmarks**\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155
280 |
281 | **Skoltech Anomaly Benchmark (SKAB)**\ : https://github.com/waico/skab
282 |
283 |
284 | ----
285 |
286 |
287 | 4. Papers
288 | ---------
289 |
290 | 4.1. Overview & Survey Papers
291 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
292 |
293 | Papers are sorted by the publication year.
294 |
295 | ====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
296 | Paper Title Venue Year Ref Materials
297 | ====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
298 | 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>`_
299 | Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>`_
300 | A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] <https://arxiv.org/pdf/1503.01158.pdf>`_
301 | 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>`_
302 | 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>`_
303 | 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>`_
304 | Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>`_
305 | Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3297314>`_
306 | Progress in Outlier Detection Techniques: A Survey IEEE Access 2019 [#Wang2019Progress]_ `[PDF] <https://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf>`_
307 | Deep learning for anomaly detection: A survey Preprint 2019 [#Chalapathy2019Deep]_ `[PDF] <https://arxiv.org/pdf/1901.03407.pdf>`_
308 | Anomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [#Bulusu2020Deep]_ `[PDF] <https://arxiv.org/pdf/2003.06979.pdf>`_
309 | 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>`_
310 | Deep Learning for Anomaly Detection: A Review CSUR 2021 [#Pang2020Deep]_ `[PDF] <https://arxiv.org/pdf/2007.02500.pdf>`_
311 | A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_
312 | 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>`_
313 | 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>`_
314 | Self-Supervised Anomaly Detection: A Survey and Outlook Preprint 2022 [#Hojjati2022Self]_ `[PDF] <https://arxiv.org/pdf/2205.05173.pdf>`_
315 | Weakly supervised anomaly detection: A survey Preprint 2023 [#Jiang2023weakly]_ `[PDF] <https://arxiv.org/abs/2302.04549>`_, `[PDF] <https://github.com/yzhao062/wsad>`_
316 | AD-LLM: Benchmarking Large Language Models for Anomaly Detection Preprint 2024 [#Yang2024ADLLM]_ `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_
317 | Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey Preprint 2024 [#Xu2024LLMsurvey]_ `[PDF] <https://arxiv.org/abs/2409.01980>`_
318 | ====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
319 |
320 | 4.2. Key Algorithms
321 | ^^^^^^^^^^^^^^^^^^^
322 |
323 | All these algorithms are available in `Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>`_.
324 |
325 | ==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================
326 | Abbreviation Paper Title Venue Year Ref Materials
327 | ==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================
328 | 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>`_
329 | 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>`_
330 | LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] <http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_
331 | IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_
332 | OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_
333 | AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_
334 | COPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [#Li2020COPOD]_ `[PDF] <https://arxiv.org/abs/2009.09463>`_
335 | ECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions TKDE 2022 [#Li2021ECOD]_ `[PDF] <https://arxiv.org/abs/2201.00382>`_
336 | ==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================
337 |
338 | 4.3. Graph & Network Outlier Detection
339 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
340 |
341 | ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================
342 | Paper Title Venue Year Ref Materials
343 | ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================
344 | Graph based anomaly detection and description: a survey DMKD 2015 [#Akoglu2015Graph]_ `[PDF] <https://arxiv.org/pdf/1404.4679.pdf>`_
345 | Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ `[PDF] <https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347>`_
346 | Outlier detection in graphs: On the impact of multiple graph models ComSIS 2019 [#Campos2019Outlier]_ `[PDF] <http://www.comsis.org/pdf.php?id=wims-8671>`_
347 | A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_
348 | ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================
349 |
350 |
351 | 4.4. Time Series Outlier Detection
352 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
353 |
354 | ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
355 | Paper Title Venue Year Ref Materials
356 | ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
357 | 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>`_
358 | 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>`_
359 | Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_
360 | 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>`_
361 | 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>`_
362 | Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection NeurIPS 2023 [#Wang2023Drift]_ `[PDF] <https://openreview.net/pdf?id=aW5bSuduF1>`_, `[Code] <https://github.com/ForestsKing/D3R>`_
363 | ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
364 |
365 |
366 | 4.5. Feature Selection in Outlier Detection
367 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
368 |
369 | ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================
370 | Paper Title Venue Year Ref Materials
371 | ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================
372 | 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>`_
373 | 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>`_
374 | ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================
375 |
376 |
377 | 4.6. High-dimensional & Subspace Outliers
378 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
379 |
380 | ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================
381 | Paper Title Venue Year Ref Materials
382 | ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================
383 | 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>`_
384 | Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_
385 | Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 [#Radovanovic2015Reverse]_ `[PDF] <https://ieeexplore.ieee.org/document/6948273>`_, `[SLIDES] <https://pdfs.semanticscholar.org/c8aa/832362422418287ff56793c780b425afa93f.pdf>`_
386 | Outlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ `[PDF] <http://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf>`_
387 | ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================
388 |
389 |
390 | 4.7. Outlier Ensembles
391 | ^^^^^^^^^^^^^^^^^^^^^^
392 |
393 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
394 | Paper Title Venue Year Ref Materials
395 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
396 | Outlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ `[PDF] <https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf>`_
397 | 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>`_
398 | 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>`_
399 | LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66>`_
400 | 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>`_
401 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
402 |
403 | 4.8. Outlier Detection in Evolving Data
404 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
405 |
406 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
407 | Paper Title Venue Year Ref Materials
408 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
409 | 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>`_
410 | 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>`_
411 | 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/>`_
412 | 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>`_
413 | 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>`_
414 | 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>`_
415 | 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>`_
416 | 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>`_
417 | 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>`_
418 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
419 |
420 |
421 | 4.9. Representation Learning in Outlier Detection
422 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
423 |
424 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
425 | Paper Title Venue Year Ref Materials
426 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
427 | Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_
428 | Learning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_
429 | XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] <https://arxiv.org/abs/1912.00290>`_
430 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
431 |
432 |
433 | 4.10. Interpretability
434 | ^^^^^^^^^^^^^^^^^^^^^^
435 |
436 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
437 | Paper Title Venue Year Ref Materials
438 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
439 | 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>`_
440 | Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf>`_
441 | Contextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ `[PDF] <https://arxiv.org/pdf/1711.10589.pdf>`_
442 | Mining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ `[PDF] <http://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf>`_
443 | Discriminative features for identifying and interpreting outliers ICDE 2014 [#Dang2014Discriminative]_ `[PDF] <https://ieeexplore.ieee.org/abstract/document/6816642>`_
444 | Sequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3230666>`_
445 | A Survey on Explainable Anomaly Detection TKDD 2023 [#Li2023XAD]_ `[HTML] <https://dl.acm.org/doi/10.1145/3609333>`_
446 | Explainable Contextual Anomaly Detection Using Quantile Regression Forests DMKD 2023 [#Li2023QCAD]_ `[HTML] <https://link.springer.com/article/10.1007/s10618-023-00967-z>`_
447 | Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network WWW 2021 [#Xu2021Beyond]_ `[PDF] <https://jiansonglei.github.io/files/21WWW.pdf>`_
448 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
449 |
450 |
451 | 4.11. Outlier Detection with Neural Networks
452 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
453 |
454 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
455 | Paper Title Venue Year Ref Materials
456 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
457 | 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>`_
458 | 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>`_
459 | 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>`_
460 | 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>`_
461 | Deep Anomaly Detection with Outlier Exposure ICLR 2019 [#Hendrycks2019Deep]_ `[PDF] <https://arxiv.org/pdf/1812.04606.pdf>`_, `[Code] <https://github.com/hendrycks/outlier-exposure>`_
462 | 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>`_,
463 | 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>`_
464 | Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning ICML 2023 [#Xu2023Fascinating]_ `[PDF] <https://arxiv.org/abs/2305.16114>`_, `[Code] <https://github.com/xuhongzuo/scale-learning>`_
465 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
466 |
467 |
468 | 4.12. Active Anomaly Detection
469 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
470 |
471 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
472 | Paper Title Venue Year Ref Materials
473 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
474 | 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>`_
475 | 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>`_
476 | Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_
477 | Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [#Zha2020Meta]_ `[PDF] <https://arxiv.org/pdf/2009.07415.pdf>`_
478 | A3: Activation Anomaly Analysis ECML-PKDD 2020 [#Sperl2021A3]_ `[PDF] <https://arxiv.org/pdf/2003.01801>`_, `[Code] <https://github.com/Fraunhofer-AISEC/A3>`_
479 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
480 |
481 |
482 | 4.13. Interactive Outlier Detection
483 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
484 |
485 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
486 | Paper Title Venue Year Ref Materials
487 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
488 | 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>`_
489 | Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_
490 | eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_
491 | Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_
492 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
493 |
494 |
495 | 4.14. Outlier Detection in Other fields
496 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
497 |
498 | ============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
499 | Field Paper Title Venue Year Ref Materials
500 | ============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
501 | **Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_
502 | ============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
503 |
504 |
505 | 4.15. Outlier Detection Applications
506 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
507 |
508 | ======================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
509 | Field Paper Title Venue Year Ref Materials
510 | ======================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
511 | **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>`_
512 | **Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] <https://www2.cs.uh.edu/~acl/cs6397/Doc/2009-Elsevier-Anomaly-based%20network%20intrusion%20detection.pdf>`_
513 | **Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] <https://www.sciencedirect.com/science/article/abs/pii/S0167739X15000023>`_
514 | **Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_
515 | **Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_
516 | **Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_
517 | **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>`_
518 | **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>`_
519 | ======================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
520 |
521 |
522 | 4.16. Automated Outlier Detection
523 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
524 |
525 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
526 | Paper Title Venue Year Ref Materials
527 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
528 | 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>`_
529 | AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning ICDE 2020 [#Li2020AutoOD]_ `[PDF] <https://arxiv.org/pdf/2006.11321.pdf>`_
530 | Automatic Unsupervised Outlier Model Selection NeurIPS 2021 [#Zhao2020Automating]_ `[PDF] <https://openreview.net/forum?id=KCd-3Pz8VjM>`_, `[Code] <https://github.com/yzhao062/MetaOD>`_
531 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
532 |
533 |
534 | 4.17. Machine Learning Systems for Outlier Detection
535 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
536 |
537 | This section summarizes a list of systems for outlier detection, which may
538 | overlap with the section of tools and libraries.
539 |
540 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
541 | Paper Title Venue Year Ref Materials
542 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
543 | 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>`_
544 | 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>`_
545 | TOD: Tensor-based Outlier Detection Preprint 2021 [#Zhao2021TOD]_ `[PDF] <https://arxiv.org/pdf/2110.14007.pdf>`_, `[Code] <https://github.com/yzhao062/pytod>`_
546 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
547 |
548 |
549 |
550 | 4.18. Fairness and Bias in Outlier Detection
551 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
552 |
553 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
554 | Paper Title Venue Year Ref Materials
555 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
556 | A Framework for Determining the Fairness of Outlier Detection ECAI 2020 [#Davidson2020A]_ `[PDF] <https://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf>`_
557 | FAIROD: Fairness-aware Outlier Detection AIES 2021 [#Shekhar2021FAIROD]_ `[PDF] <https://arxiv.org/pdf/2012.03063.pdf>`_
558 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
559 |
560 |
561 |
562 | 4.19. Isolation-Based Methods
563 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
564 |
565 | ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================
566 | Paper Title Venue Year Ref Materials
567 | ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================
568 | Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_
569 | 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>`_
570 | Extended Isolation Forest TKDE 2019 [#Hariri2019Extended]_ `[PDF] <https://arxiv.org/pdf/1811.02141.pdf>`_, `[Code] <https://github.com/sahandha/eif>`_
571 | 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>`_
572 | Deep Isolation Forest for Anomaly Detection TKDE 2023 [#Xu2023Deep]_ `[PDF] <https://arxiv.org/abs/2206.06602>`_, `[Code] <https://github.com/xuhongzuo/deep-iforest>`_
573 | ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================
574 |
575 |
576 | 4.20. Weakly-Supervised Methods
577 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
578 |
579 | ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================
580 | Paper Title Venue Year Ref Materials
581 | ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================
582 | XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] <https://arxiv.org/abs/1912.00290>`_
583 | Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection TNNLS 2021 [#Zhou2021Feature]_ `[PDF] <https://arxiv.org/pdf/2105.10500.pdf>`_, `[Code] <https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection>`_
584 | ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================
585 |
586 |
587 |
588 | 4.21. Emerging and Interesting Topics
589 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
590 |
591 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
592 | Paper Title Venue Year Ref Materials
593 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
594 | Clustering with Outlier Removal TKDE 2019 [#Liu2018Clustering]_ `[PDF] <https://arxiv.org/pdf/1801.01899.pdf>`_
595 | 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>`_
596 | 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>`_
597 | AD-LLM: Benchmarking Large Language Models for Anomaly Detection Preprint 2024 [#Yang2024ADLLM]_ `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_
598 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
599 |
600 |
601 | ----
602 |
603 | 5. Key Conferences/Workshops/Journals
604 | -------------------------------------
605 |
606 | 5.1. Conferences & Workshops
607 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
608 |
609 | Key data mining conference **deadlines**, **historical acceptance rates**, and more
610 | can be found `data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>`_.
611 |
612 |
613 | `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/>`_.
614 |
615 | `ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>`_
616 |
617 | `The Web Conference (WWW) <https://www2018.thewebconf.org/>`_
618 |
619 | `IEEE International Conference on Data Mining (ICDM) <https://icdm2024.org//>`_
620 |
621 | `SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>`_
622 |
623 | `IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>`_
624 |
625 | `ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>`_
626 |
627 | `ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>`_
628 |
629 | `The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>`_
630 |
631 | `The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>`_
632 |
633 | 5.2. Journals
634 | ^^^^^^^^^^^^^
635 |
636 | `ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>`_
637 |
638 | `IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>`_
639 |
640 | `ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>`_
641 |
642 | `Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>`_
643 |
644 | `Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>`_
645 |
646 | ----
647 |
648 | References
649 | ----------
650 |
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748 |
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756 |
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778 |
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780 |
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782 |
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794 |
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796 |
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812 |
813 | .. [#Salehi2018A] Salehi, Mahsa & Rashidi, Lida. (2018). A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]. *ACM SIGKDD Explorations Newsletter*. 20. 13-23.
814 |
815 | .. [#Salehi2021A] Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M.H., Sabokrou, M., 2021. A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. arXiv preprint arXiv:2110.14051.
816 |
817 | .. [#Scholkopf2001Estimating] Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. *Neural Computation*, 13(7), pp.1443-1471.
818 |
819 | .. [#Sehwag2021SSD] Sehwag, V., Chiang, M., Mittal, P., 2021. SSD: A Unified Framework for Self-Supervised Outlier Detection. *International Conference on Learning Representations (ICLR)*.
820 |
821 | .. [#Shekhar2021FAIROD] Shekhar, S., Shah, N. and Akoglu, L., 2021. FAIROD: Fairness-aware Outlier Detection. AAAI/ACM Conference on AI, Ethics, and Society (AIES).
822 |
823 | .. [#Siddiqui2019Sequential] Siddiqui, M.A., Fern, A., Dietterich, T.G. and Wong, W.K., 2019. Sequential Feature Explanations for Anomaly Detection. *ACM Transactions on Knowledge Discovery from Data (TKDD)*, 13(1), p.1.
824 |
825 | .. [#Sperl2021A3] Sperl, P., Schulze, J.-P., and Böttinger, K., 2021. Activation Anomaly Analysis. *European Conference on Machine Learning and Data Mining (ECML-PKDD) 2020*.
826 |
827 | .. [#Suri2019Research] Suri, N.R. and Athithan, G., 2019. Research Issues in Outlier Detection. In *Outlier Detection: Techniques and Applications*, pp. 29-51. Springer, Cham.
828 |
829 | .. [#Tang2015Mining] Tang, G., Pei, J., Bailey, J. and Dong, G., 2015. Mining multidimensional contextual outliers from categorical relational data. *Intelligent Data Analysis*, 19(5), pp.1171-1192.
830 |
831 | .. [#Tang2023GADBench] Tang, J., Hua, F., Gao, Z., Zhao, P. and Li, J., 2023. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. *NeurIPS*, Datasets and Benchmarks Track.
832 |
833 | .. [#Ting2018Which] Ting, KM., Aryal, S. and Washio, T., 2018, Which Anomaly Detector should I use? *Tutorial at ICDM 2018*.
834 |
835 | .. [#Ting2020Isolation] Ting, Kai Ming, Bi-Cun Xu, Takashi Washio, and Zhi-Hua Zhou. "Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection." In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, pp. 198-206. 2020.
836 |
837 | .. [#Wang2019Effective] Wang, S., Zeng, Y., Liu, X., Zhu, E., Yin, J., Xu, C. and Kloft, M., 2019. Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. In *33rd Conference on Neural Information Processing Systems*.
838 |
839 | .. [#Wang2019Progress] Wang, H., Bah, M.J. and Hammad, M., 2019. Progress in Outlier Detection Techniques: A Survey. *IEEE Access*, 7, pp.107964-108000.
840 |
841 | .. [#Wang2020Deep] Wang, R., Nie, K., Chang, Y. J., Gong, X., Wang, T., Yang, Y., Long, B., 2020. Deep Learning for Anomaly Detection. *Tutorial at KDD 2020*.
842 |
843 | .. [#WellerFahy2015A] Weller-Fahy, D.J., Borghetti, B.J. and Sodemann, A.A., 2015. A survey of distance and similarity measures used within network intrusion anomaly detection. *IEEE Communications Surveys & Tutorials*\ , 17(1), pp.70-91.
844 |
845 | .. [#Xu2021Beyond] Xu, H., Wang, Y., Jian, S., Huang, Z., Wang, Y., Liu, N. and Li, F., 2021, April. Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network. In *Proceedings of the Web Conference* 2021 (pp. 1328-1339).
846 |
847 | .. [#Xu2023Deep] Xu, H., Pang, G., Wang, Y., Wang, Y., 2023. Deep Isolation Forest for Anomaly Detection. *IEEE Transactions on Knowledge and Data Engineering*.
848 |
849 | .. [#Xu2023Fascinating] Xu, H., Wang, Y., Wei, J., Jian, S., Li, Y., Liu, N., 2023. Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning. *International Conference on Machine Learning (ICML)*.
850 |
851 | .. [#Xu2024LLMsurvey] Xu, R. and Ding, K., 2024. Large language models for anomaly and out-of-distribution detection: A survey. arXiv preprint arXiv:2409.01980.
852 |
853 | .. [#Yoon2019NETS] Yoon, S., Lee, J. G., & Lee, B. S., 2019. NETS: extremely fast outlier detection from a data stream via set-based processing. Proceedings of the VLDB Endowment, 12(11), 1303-1315.
854 |
855 | .. [#Yoon2020STARE] Yoon, S., Lee, J. G., & Lee, B. S., 2020. Ultrafast local outlier detection from a data stream with stationary region skipping. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1181-1191)
856 |
857 | .. [#Yoon2021MDUAL] Yoon, S., Shin, Y., Lee, J. G., & Lee, B. S. (2021, June). Multiple dynamic outlier-detection from a data stream by exploiting duality of data and queries. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD).
858 |
859 | .. [#Yoon2022ARCUS] Yoon, S., Lee, Y., Lee, J.G. and Lee, B.S., 2022, August. Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2347-2357).
860 |
861 | .. [#Yu2015Glad] Yu, R., He, X. and Liu, Y., 2015. GLAD: group anomaly detection in social media analysis. *ACM Transactions on Knowledge Discovery from Data (TKDD)*\ , 10(2), p.18.
862 |
863 | .. [#Yu2016A] Yu, R., Qiu, H., Wen, Z., Lin, C. and Liu, Y., 2016. A survey on social media anomaly detection. *ACM SIGKDD Explorations Newsletter*\ , 18(1), pp.1-14.
864 |
865 | .. [#Yuan2024Trustworthy] Yuan, S., Xu, D. and Wu, X., 2024 Trustworthy Anomaly Detection. *Tutorial at SDM 2024*.
866 |
867 | .. [#Zha2020Meta] Zha, D., Lai, K.H., Wan, M. and Hu, X., 2020. Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. *ICDM*.
868 |
869 | .. [#Zhao2018Xgbod] Zhao, Y. and Hryniewicki, M.K., 2018, July. XGBOD: improving supervised outlier detection with unsupervised representation learning. In *2018 International Joint Conference on Neural Networks (IJCNN)*. IEEE.
870 |
871 | .. [#Zhao2019LSCP] Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 585-593. Society for Industrial and Applied Mathematics.
872 |
873 | .. [#Zhao2019PYOD] Zhao, Y., Nasrullah, Z. and Li, Z., PyOD: A Python Toolbox for Scalable Outlier Detection. *Journal of Machine Learning Research*, 20, pp.1-7.
874 |
875 | .. [#Zhao2020Automating] Zhao, Y., Rossi, R.A. and Akoglu, L., 2021. Automatic Unsupervised Outlier Model Selection. *Advances in Neural Information Processing Systems*.
876 |
877 | .. [#Zhao2021SUOD] Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C. and Wang, Y., 2021. SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. *Proceedings of Machine Learning and Systems (MLSys)*.
878 |
879 | .. [#Zhao2021TOD] Zhao, Y., Chen, G.H. and Jia, Z., 2021. TOD: Tensor-based Outlier Detection. arXiv preprint arXiv:2110.14007.
880 |
881 | .. [#Zhou2019AnomalyNet] Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y. and Goh, R.S.M., 2019. AnomalyNet: An anomaly detection network for video surveillance. *IEEE Transactions on Information Forensics and Security*.
882 |
883 | .. [#Zhou2021Feature] Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465.
884 |
885 | .. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*.
886 |
887 | .. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\ , 5(5), pp.363-387.
888 |
889 | .. [#Zimek2014Ensembles] Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. *ACM Sigkdd Explorations Newsletter*\ , 15(1), pp.11-22.
890 |
891 | .. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR).
892 |
893 | .. [#Wang2023Drift] Wang, C., Zhuang, Z., Qi, Q., Wang, J., Wang, X., Sun, H., & Liao, J. (2023). Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. Advances in Neural Information Processing Systems, 36.
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/README_CN.rst:
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1 | 异常检测学习资源（Anomaly Detection Learning Resources）
2 | ====================================================
3 |
4 | .. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg
5 | :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers
6 | :alt: GitHub stars
7 |
8 |
9 | .. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue
10 | :target: https://github.com/yzhao062/anomaly-detection-resources/network
11 | :alt: GitHub forks
12 |
13 |
14 | .. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue
15 | :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE
16 | :alt: License
17 |
18 |
19 | .. image:: https://img.shields.io/badge/link-996.icu-red.svg
20 | :target: https://github.com/996icu/996.ICU
21 | :alt: 996.ICU
22 |
23 |
24 | ----
25 |
26 | `异常检测 (anomaly detection) <https://zh.wikipedia.org/wiki/%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B>`_
27 | (又名 Outlier Detection) 是一个重要但非常有挑战性的领域。异常检测的目标主要是找到数据中
28 | 偏离于主要分布的案例--它在很多领域都有重要意义，包括「信用卡诈骗检测」、「网络入侵检测」、
29 | 「机械故障检测」等。
30 |
31 | 这个仓库中收藏了关于异常检测的：
32 |
33 |
34 | #. 专业书籍与学术论文
35 | #. 在线课程与视频
36 | #. 异常检测数据集
37 | #. 开源与商业工具库
38 | #. 重要的会议与期刊
39 |
40 |
41 | **更多内容会被陆续添加到当前仓库中来**。
42 | 请建议/推荐相关资源，你可以选择提交issue report、pull request或者给我发邮件 (zhaoy@cmu.edu)。
43 | Enjoy reading！
44 |
45 | ----
46 |
47 | 目录
48 | -----------------
49 |
50 |
51 | * `1. 书籍 & 教程 <#1-书籍--教程>`_
52 |
53 | * `1.1. 书籍 <#11-书籍>`_
54 | * `1.2. 教程 <#12-教程>`_
55 |
56 | * `2. Courses/Seminars/Videos <#2-coursesseminarsvideos>`_
57 | * `3. Toolbox & Datasets <#3-toolbox--datasets>`_
58 |
59 | * `3.1. Multivariate data outlier detection <#31-multivariate-data>`_
60 | * `3.2. Time series outlier detection <#32-time-series-outlier-detection>`_
61 | * `3.3. Datasets <#33-datasets>`_
62 |
63 | * `4. Papers <#4-papers>`_
64 |
65 | * `4.1. Overview & Survey Papers <#41-overview--survey-papers>`_
66 | * `4.2. Key Algorithms <#42-key-algorithms>`_
67 | * `4.3. Graph & Network Outlier Detection <#43-graph--network-outlier-detection>`_
68 | * `4.4. Time Series Outlier Detection <#44-time-series-outlier-detection>`_
69 | * `4.5. Feature Selection in Outlier Detection <#45-feature-selection-in-outlier-detection>`_
70 | * `4.6. High-dimensional & Subspace Outliers <#46-high-dimensional--subspace-outliers>`_
71 | * `4.7. Outlier Ensembles <#47-outlier-ensembles>`_
72 | * `4.8. Outlier Detection in Evolving Data <#48-outlier-detection-in-evolving-data>`_
73 | * `4.9. Representation Learning in Outlier Detection <#49-representation-learning-in-outlier-detection>`_
74 | * `4.10. Interpretability <#410-interpretability>`_
75 | * `4.11. Outlier Detection with Neural Networks <#411-outlier-detection-with-neural-networks>`_
76 | * `4.12. Active Anomaly Detection <#412-active-anomaly-detection>`_
77 | * `4.13. Interactive Outlier Detection <#413-interactive-outlier-detection>`_
78 | * `4.14. Outlier Detection in Other fields <#414-outlier-detection-in-other-fields>`_
79 | * `4.15. Outlier Detection Applications <#415-outlier-detection-applications>`_
80 |
81 | * `5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>`_
82 |
83 | * `5.1. Conferences & Workshops <#51-conferences--workshops>`_
84 | * `5.2. Journals <#52-journals>`_
85 |
86 |
87 | ----
88 |
89 | 1. 书籍 & 教程
90 | -------------
91 |
92 | 1.1. 书籍
93 | ^^^^^^^^
94 |
95 | `Outlier Analysis <https://www.springer.com/gp/book/9781461463955>`_
96 | 作者: Charu Aggarwal: 经典异常检测教科书，内容涵盖了大部分相关算法与应用。异常检测领域人士必读。
97 | `[预览.pdf] <http://charuaggarwal.net/outlierbook.pdf>`_
98 |
99 | `Outlier Ensembles: An Introduction <https://www.springer.com/gp/book/9783319547640>`_
100 | 作者: Charu Aggarwal and Saket Sathe: 非常权威的集成异常检测教科书。
101 |
102 | `Data Mining: Concepts and Techniques (3rd) <https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1>`_
103 | 作者: 韩家炜 (Jiawei Han) and Micheline Kamber and Jian Pei (裴健): 该书第十二章讨论了异常检测技术。 `[Google Search] <https://www.google.com/search?&q=data+mining+jiawei+han&oq=data+ming+jiawei>`_
104 |
105 | 1.2. 教程
106 | ^^^^^^^^
107 |
108 | ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================
109 | Tutorial Title Venue Year Ref Materials
110 | ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================
111 | Outlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ `[PDF] <https://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf>`_
112 | Anomaly Detection: A Tutorial ICDM 2011 [#Chawla2011Anomaly]_ `[PDF] <http://webdocs.cs.ualberta.ca/~icdm2011/downloads/ICDM2011_anomaly_detection_tutorial.pdf>`_
113 | Data mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ `[Video] <http://videolectures.net/ecmlpkdd08_lazarevic_dmfa/>`_
114 | ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================
115 |
116 | ----
117 |
118 | 2. Courses/Seminars/Videos
119 | --------------------------
120 |
121 | **Coursera Introduction to Anomaly Detection (by IBM)**\ :
122 | `[See Video] <https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection>`_
123 |
124 | **Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\ :
125 | `[See Video] <https://www.coursera.org/learn/real-time-cyber-threat-detection>`_
126 |
127 | **Coursera Machine Learning by Andrew Ng also partly covers the topic**\ :
128 |
129 |
130 | * `Anomaly Detection vs. Supervised Learning <https://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning>`_
131 | * `Developing and Evaluating an Anomaly Detection System <https://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system>`_
132 |
133 | **Udemy Outlier Detection Algorithms in Data Mining and Data Science**\ :
134 | `[See Video] <https://www.udemy.com/outlier-detection-techniques/>`_
135 |
136 | **Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\ :
137 | `[See Video] <http://web.stanford.edu/class/cs259d/>`_
138 |
139 | ----
140 |
141 | 3. Toolbox & Datasets
142 | ---------------------
143 |
144 | 3.1. Multivariate Data
145 | ^^^^^^^^^^^^^^^^^^^^^^
146 |
147 | [**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.
148 |
149 | [**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.
150 |
151 | [**Java**] `ELKI: Environment for Developing KDD-Applications Supported by Index-Structures <https://elki-project.github.io/>`_\ :
152 | 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.
153 |
154 | [**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.
155 |
156 | [**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.
157 |
158 | [**Matlab**] `Anomaly Detection Toolbox - Beta <http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/>`_\ : A collection of popular outlier detection algorithms in Matlab.
159 |
160 |
161 | 3.2. Time series outlier detection
162 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
163 |
164 |
165 | [**Python**] `datastream.io <https://github.com/MentatInnovations/datastream.io>`_\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.
166 |
167 | [**Python**] `skyline <https://github.com/earthgecko/skyline>`_\ : Skyline is a near real time anomaly detection system.
168 |
169 | [**Python**] `banpei <https://github.com/tsurubee/banpei>`_\ : Banpei is a Python package of the anomaly detection.
170 |
171 | [**Python**] `telemanom <https://github.com/khundman/telemanom>`_\ : A framework for using LSTMs to detect anomalies in multivariate time series data.
172 |
173 | [**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.
174 |
175 | [**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.
176 |
177 |
178 | 3.3. Datasets
179 | ^^^^^^^^^^^^^
180 |
181 | **ELKI Outlier Datasets**\ : https://elki-project.github.io/datasets/outlier
182 |
183 | **Outlier Detection DataSets (ODDS)**\ : http://odds.cs.stonybrook.edu/#table1
184 |
185 | **Unsupervised Anomaly Detection Dataverse**\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF
186 |
187 | **Anomaly Detection Meta-Analysis Benchmarks**\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155
188 |
189 | ----
190 |
191 | 4. Papers
192 | ---------
193 |
194 | 4.1. Overview & Survey Papers
195 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
196 |
197 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
198 | Paper Title Venue Year Ref Materials
199 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
200 | 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>`_
201 | Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>`_
202 | A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] <https://arxiv.org/pdf/1503.01158.pdf>`_
203 | 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>`_
204 | 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>`_
205 | 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>`_
206 | Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>`_
207 | Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3297314>`_
208 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
209 |
210 | 4.2. Key Algorithms
211 | ^^^^^^^^^^^^^^^^^^^
212 |
213 | ==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================
214 | Abbreviation Paper Title Venue Year Ref Materials
215 | ==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================
216 | 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>`_
217 | 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>`_
218 | LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] <http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_
219 | IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_
220 | OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_
221 | AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_
222 | ==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================
223 |
224 | 4.3. Graph & Network Outlier Detection
225 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
226 |
227 | ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================
228 | Paper Title Venue Year Ref Materials
229 | ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================
230 | Graph based anomaly detection and description: a survey DMKD 2015 [#Akoglu2015Graph]_ `[PDF] <https://arxiv.org/pdf/1404.4679.pdf>`_
231 | Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ `[PDF] <https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347>`_
232 | ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================
233 |
234 |
235 | 4.4. Time Series Outlier Detection
236 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
237 |
238 | ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
239 | Paper Title Venue Year Ref Materials
240 | ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
241 | 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>`_
242 | 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>`_
243 | Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_
244 | 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>`_
245 | 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>`_
246 | Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection NeurIPS 2023 [#Wang2023Drift]_ `[PDF] <https://openreview.net/pdf?id=aW5bSuduF1>`_, `[Code] <https://github.com/ForestsKing/D3R>`_
247 | ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
248 |
249 |
250 | 4.5. Feature Selection in Outlier Detection
251 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
252 |
253 | ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================
254 | Paper Title Venue Year Ref Materials
255 | ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================
256 | 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>`_
257 | 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>`_
258 | ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================
259 |
260 |
261 | 4.6. High-dimensional & Subspace Outliers
262 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
263 |
264 | ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================
265 | Paper Title Venue Year Ref Materials
266 | ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================
267 | 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>`_
268 | Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_
269 | 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>`_
270 | Outlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ `[PDF] <http://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf>`_
271 | ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================
272 |
273 |
274 | 4.7. Outlier Ensembles
275 | ^^^^^^^^^^^^^^^^^^^^^^
276 |
277 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
278 | Paper Title Venue Year Ref Materials
279 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
280 | Outlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ `[PDF] <https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf>`_
281 | 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>`_
282 | 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>`_
283 | LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66>`_
284 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
285 |
286 | 4.8. Outlier Detection in Evolving Data
287 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
288 |
289 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
290 | Paper Title Venue Year Ref Materials
291 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
292 | 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>`_
293 | 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>`_
294 | 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/>`_
295 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
296 |
297 |
298 | 4.9. Representation Learning in Outlier Detection
299 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
300 |
301 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
302 | Paper Title Venue Year Ref Materials
303 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
304 | Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_
305 | Learning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_
306 | XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] <https://www.yuezhao.me/s/edited_XGBOD.pdf>`_
307 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
308 |
309 |
310 | 4.10. Interpretability
311 | ^^^^^^^^^^^^^^^^^^^^^^
312 |
313 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
314 | Paper Title Venue Year Ref Materials
315 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
316 | 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>`_
317 | Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf>`_
318 | Contextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ `[PDF] <https://arxiv.org/pdf/1711.10589.pdf>`_
319 | Mining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ `[PDF] <http://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf>`_
320 | 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>`_
321 | Sequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3230666>`_
322 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
323 |
324 |
325 | 4.11. Outlier Detection with Neural Networks
326 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
327 |
328 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
329 | Paper Title Venue Year Ref Materials
330 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
331 | 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>`_
332 | MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks Preprint 2019 [#Li2019MAD]_ `[PDF] <https://arxiv.org/pdf/1901.04997.pdf>`_, `[Code] <https://github.com/LiDan456/MAD-GANs>`_
333 | 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>`_
334 | 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>`_
335 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
336 |
337 |
338 | 4.12. Active Anomaly Detection
339 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
340 |
341 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
342 | Paper Title Venue Year Ref Materials
343 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
344 | 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>`_
345 | 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>`_
346 | Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_
347 | ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
348 |
349 |
350 | 4.13. Interactive Outlier Detection
351 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
352 |
353 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
354 | Paper Title Venue Year Ref Materials
355 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
356 | 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>`_
357 | Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_
358 | eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_
359 | Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_
360 | ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
361 |
362 |
363 | 4.14. Outlier Detection in Other fields
364 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
365 |
366 | ============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
367 | Field Paper Title Venue Year Ref Materials
368 | ============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
369 | **Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_
370 | ============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
371 |
372 |
373 | 4.15. Outlier Detection Applications
374 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
375 |
376 | =================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
377 | Field Paper Title Venue Year Ref Materials
378 | =================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
379 | **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>`_
380 | **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>`_
381 | **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>`_
382 | **Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_
383 | **Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_
384 | **Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_
385 | **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>`_
386 | =================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
387 |
388 |
389 | ----
390 |
391 | 5. Key Conferences/Workshops/Journals
392 | -------------------------------------
393 |
394 | 5.1. Conferences & Workshops
395 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
396 |
397 | Key data mining conference **deadlines**, **historical acceptance rates**, and more
398 | can be found `data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>`_.
399 |
400 |
401 | `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 2018 <https://www.andrew.cmu.edu/user/lakoglu/odd/index.html>`_.
402 |
403 | `ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>`_
404 |
405 | `The Web Conference (WWW) <https://www2018.thewebconf.org/>`_
406 |
407 | `IEEE International Conference on Data Mining (ICDM) <http://icdm2018.org/>`_
408 |
409 | `SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>`_
410 |
411 | `IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>`_
412 |
413 | `ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>`_
414 |
415 | `ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>`_
416 |
417 | `The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>`_
418 |
419 | `The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>`_
420 |
421 | 5.2. Journals
422 | ^^^^^^^^^^^^^
423 |
424 | `ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>`_
425 |
426 | `IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>`_
427 |
428 | `ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>`_
429 |
430 | `Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>`_
431 |
432 | `Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>`_
433 |
434 | ----
435 |
436 | References
437 | ----------
438 |
439 | .. [#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.
440 |
441 | .. [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. *ACM SIGKDD Explorations Newsletter*\ , 14(2), pp.49-58.
442 |
443 | .. [#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.
444 |
445 | .. [#Ahmad2017Unsupervised] Ahmad, S., Lavin, A., Purdy, S. and Agha, Z., 2017. Unsupervised real-time anomaly detection for streaming data. *Neurocomputing*, 262, pp.134-147.
446 |
447 | .. [#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.
448 |
449 | .. [#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.
450 |
451 | .. [#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)*.
452 |
453 | .. [#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.
454 |
455 | .. [#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.
456 |
457 | .. [#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.
458 |
459 | .. [#Chandola2009Anomaly] Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. *ACM computing surveys* , 41(3), p.15.
460 |
461 | .. [#Chawla2011Anomaly] Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. *Tutorial at ICDM 2011*.
462 |
463 | .. [#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.
464 |
465 | .. [#Dang2014Discriminative] Dang, X.H., Assent, I., Ng, R.T., Zimek, A. and Schubert, E., 2014, March. Discriminative features for identifying and interpreting outliers. In *International Conference on Data Engineering (ICDE)*. IEEE.
466 |
467 | .. [#Das2019Active] Das, S., Islam, M.R., Jayakodi, N.K. and Doppa, J.R., 2019. Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability. arXiv preprint arXiv:1901.08930.
468 |
469 | .. [#Ding2019Interactive] Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In *Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining*, pp. 357-365. ACM.
470 |
471 | .. [#Djenouri2018Outlier] Djenouri, Y. and Zimek, A., 2018, June. Outlier detection in urban traffic data. In *Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics*. ACM.
472 |
473 | .. [#Domingues2018A] Domingues, R., Filippone, M., Michiardi, P. and Zouaoui, J., 2018. A comparative evaluation of outlier detection algorithms: Experiments and analyses. *Pattern Recognition*, 74, pp.406-421.
474 |
475 | .. [#Emmott2015A] Emmott, A., Das, S., Dietterich, T., Fern, A. and Wong, W.K., 2015. A meta-analysis of the anomaly detection problem. arXiv preprint arXiv:1503.01158.
476 |
477 | .. [#Falcao2019Quantitative] Falcão, F., Zoppi, T., Silva, C.B.V., Santos, A., Fonseca, B., Ceccarelli, A. and Bondavalli, A., 2019, April. Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In *Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing*, (pp. 318-327). ACM.
478 |
479 | .. [#GarciaTeodoro2009Anomaly] Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G. and Vázquez, E., 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. *Computers & Security*\ , 28(1-2), pp.18-28.
480 |
481 | .. [#Goldstein2016A] Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. *PloS one*\ , 11(4), p.e0152173.
482 |
483 | .. [#Gupta2014Outlier] Gupta, M., Gao, J., Aggarwal, C.C. and Han, J., 2014. Outlier detection for temporal data: A survey. *IEEE Transactions on Knowledge and Data Engineering*\ , 26(9), pp.2250-2267.
484 |
485 | .. [#Hodge2004A] Hodge, V. and Austin, J., 2004. A survey of outlier detection methodologies. *Artificial intelligence review*\ , 22(2), pp.85-126.
486 |
487 | .. [#Hundman2018Detecting] Hundman, K., Constantinou, V., Laporte, C., Colwell, I. and Soderstrom, T., 2018, July. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In *Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, (pp. 387-395). ACM.
488 |
489 | .. [#Kannan2017Outlier] Kannan, R., Woo, H., Aggarwal, C.C. and Park, H., 2017, June. Outlier detection for text data. In *Proceedings of the 2017 SIAM International Conference on Data Mining*, pp. 489-497. Society for Industrial and Applied Mathematics.
490 |
491 | .. [#Kriegel2010Outlier] Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. *Tutorial at ACM SIGKDD 2010*.
492 |
493 | .. [#Lazarevic2008Data] Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. *Tutorial at ECML PKDD 2008*.
494 |
495 | .. [#Lamba2019Learning] Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 612-620. Society for Industrial and Applied Mathematics.
496 |
497 | .. [#Li2019MAD] Li, D., Chen, D., Shi, L., Jin, B., Goh, J. and Ng, S.K., 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. arXiv preprint arXiv:1901.04997.
498 |
499 | .. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE.
500 |
501 | .. [#Liu2018Contextual] Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In *International Joint Conference on Artificial Intelligence (IJCAI-18)*, pp.2461-2467.
502 |
503 | .. [#Liu2019Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative Adversarial Active Learning for Unsupervised Outlier Detection. *IEEE transactions on knowledge and data engineering*.
504 |
505 | .. [#Macha2018Explaining] Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480.
506 |
507 | .. [#Manzoor2018Outlier] Manzoor, E., Lamba, H. and Akoglu, L. Outlier Detection in Feature-Evolving Data Streams. In *24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD)*. 2018.
508 |
509 | .. [#Micenkova2015Learning] Micenková, B., McWilliams, B. and Assent, I., 2015. Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104.
510 |
511 | .. [#Gupta2018Beyond] Gupta, N., Eswaran, D., Shah, N., Akoglu, L. and Faloutsos, C., Beyond Outlier Detection: LookOut for Pictorial Explanation. *ECML PKDD 2018*.
512 |
513 | .. [#Pang2016Unsupervised] Pang, G., Cao, L., Chen, L. and Liu, H., 2016, December. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 410-419). IEEE.
514 |
515 | .. [#Pang2017Learning] Pang, G., Cao, L., Chen, L. and Liu, H., 2017, August. Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 2585-2591). AAAI Press.
516 |
517 | .. [#Pang2018Learning] Pang, G., Cao, L., Chen, L. and Liu, H., 2018. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In *24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD)*. 2018.
518 |
519 | .. [#Pelleg2005Active] Pelleg, D. and Moore, A.W., 2005. Active learning for anomaly and rare-category detection. In *Advances in neural information processing systems*\, pp. 1073-1080.
520 |
521 | .. [#Radovanovic2015Reverse] Radovanović, M., Nanopoulos, A. and Ivanović, M., 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. *IEEE transactions on knowledge and data engineering*, 27(5), pp.1369-1382.
522 |
523 | .. [#Ramaswamy2000Efficient] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM SIGMOD Record*\ , 29(2), pp. 427-438.
524 |
525 | .. [#Ranshous2015Anomaly] Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C. and Samatova, N.F., 2015. Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3), pp.223-247.
526 |
527 | .. [#Riazi2019Detecting] Riazi, M., Zaiane, O., Takeuchi, T., Maltais, A., Günther, J. and Lipsett, M., Detecting the Onset of Machine Failure Using Anomaly Detection Methods.
528 |
529 | .. [#Ro2015Outlier] Ro, K., Zou, C., Wang, Z. and Yin, G., 2015. Outlier detection for high-dimensional data. *Biometrika*, 102(3), pp.589-599.
530 |
531 | .. [#Salehi2018A] Salehi, Mahsa & Rashidi, Lida. (2018). A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]. *ACM SIGKDD Explorations Newsletter*. 20. 13-23.
532 |
533 | .. [#Scholkopf2001Estimating] Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. *Neural Computation*, 13(7), pp.1443-1471.
534 |
535 | .. [#Siddiqui2019Sequential] Siddiqui, M.A., Fern, A., Dietterich, T.G. and Wong, W.K., 2019. Sequential Feature Explanations for Anomaly Detection. *ACM Transactions on Knowledge Discovery from Data (TKDD)*, 13(1), p.1.
536 |
537 | .. [#Suri2019Research] Suri, N.R. and Athithan, G., 2019. Research Issues in Outlier Detection. In *Outlier Detection: Techniques and Applications*, pp. 29-51. Springer, Cham.
538 |
539 | .. [#Tang2015Mining] Tang, G., Pei, J., Bailey, J. and Dong, G., 2015. Mining multidimensional contextual outliers from categorical relational data. *Intelligent Data Analysis*, 19(5), pp.1171-1192.
540 |
541 | .. [#WellerFahy2015A] Weller-Fahy, D.J., Borghetti, B.J. and Sodemann, A.A., 2015. A survey of distance and similarity measures used within network intrusion anomaly detection. *IEEE Communications Surveys & Tutorials*\ , 17(1), pp.70-91.
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543 | .. [#Yu2015Glad] Yu, R., He, X. and Liu, Y., 2015. GLAD: group anomaly detection in social media analysis. *ACM Transactions on Knowledge Discovery from Data (TKDD)*\ , 10(2), p.18.
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/download.py:
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1 | #!/usr/bin/python
2 |
3 | """
4 | This script will download all papers/books and rename to proper name
5 | if there is no copyright issue.
6 |
7 | TODO: download resources by item number
8 | TODO: add exception handler for downloader
9 | """
10 | import re
11 | import pathlib
12 | import urllib.request
13 |
14 | # initialize the log directory if it does not exist
15 | pathlib.Path('resources').mkdir(parents=True, exist_ok=True)
16 |
17 | f = open('resource_urls\\papers.txt', 'r')
18 | for line in f:
19 | # print(line)
20 | line_splits = line.split(' | ')
21 |
22 | # remove all special char in file name
23 | file_name = re.sub(r'[\\/*?:"<>|]', "", line_splits[0])
24 | # strip filename length in case it is too long
25 | if len(file_name) > 255:
26 | file_name = file_name[:255]
27 | url = line_splits[1]
28 |
29 | print('Downloading', file_name, 'from', url)
30 | urllib.request.urlretrieve(url, "resources\\" + file_name + '.pdf')
31 |
32 | f.close()
33 |
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/resource_urls/papers.txt:
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1 | Anomaly detection: A survey | https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf
2 | A survey of outlier detection methodologies | https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf
3 | A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data | http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable
4 | Outlier detection for temporal data: A survey | https://pdfs.semanticscholar.org/18d1/714870fb989f32b4311892e8765f00f7098f.pdf
5 | Ensembles for unsupervised outlier detection: challenges and research questions a position paper | http://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf
6 | Outlier ensembles: position paper | https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf
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/url_checker.py:
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1 | #!/usr/bin/env python3
2 |
3 | """
4 | Script: check_urls_in_readme.py
5 |
6 | Place this script in the same folder as README.rst. When you run it,
7 | it will search for all HTTP/HTTPS links in README.rst and attempt
8 | to connect via a HEAD request. Any inaccessible links will be flagged.
9 |
10 | Usage:
11 | python check_urls_in_readme.py
12 | """
13 |
14 | import re
15 | import requests
16 |
17 | # Characters we often see trailing links in reStructuredText
18 | # e.g., https://arxiv.org/abs/2309.15376>`_ or ) or .
19 | # We iterate from the end of the string and remove them until
20 | # the last character is no longer in this set.
21 | TRAILING_PUNCT = set('`">),._')
22 |
23 | def clean_url(url: str) -> str:
24 | """
25 | Iteratively strip from the right any of the punctuation
26 | chars in TRAILING_PUNCT, often appearing in RST link syntax.
27 | """
28 | while url and url[-1] in TRAILING_PUNCT:
29 | url = url[:-1]
30 | return url
31 |
32 | def main():
33 | filename = 'README.rst' # Adjust if your RST file has another name
34 |
35 | # Read the RST file
36 | try:
37 | with open(filename, 'r', encoding='utf-8') as f:
38 | content = f.read()
39 | except FileNotFoundError:
40 | print(f"Error: Could not find {filename}. Please place this script in "
41 | f"the same directory as {filename}.")
42 | return
43 |
44 | # Regex to match http:// or https:// until whitespace or newline
45 | url_pattern = re.compile(r'http[s]?://\S+')
46 | raw_urls = url_pattern.findall(content)
47 |
48 | # Clean up trailing punctuation for each raw URL
49 | urls = set(clean_url(u) for u in raw_urls)
50 |
51 | if not urls:
52 | print("No URLs found in the file.")
53 | return
54 |
55 | # Check each URL
56 | for url in sorted(urls):
57 | try:
58 | response = requests.head(url, allow_redirects=True, timeout=5)
59 | if response.status_code < 400:
60 | print(f"[OK] {url}")
61 | else:
62 | print(f"[FAIL] {url} (Status code: {response.status_code})")
63 | except requests.RequestException as e:
64 | print(f"[FAIL] {url} (Exception: {e})")
65 |
66 | if __name__ == "__main__":
67 | main()
68 |
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