Awesome papers and Source Code for Outlier Detection

Distance-based Outlier Detection

1. Local Outlier Factor (LOF)

• Paper: LOF: identifying density-based local outliers, https://dl.acm.org/doi/10.1145/335191.335388
• Implementation: sklearn https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html.

2. Density-based Incremental LOF (DILOF)

• Paper: DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams, https://dl.acm.org/doi/abs/10.1145/3219819.3220022
• Implementation: github https://github.com/ngs00/DILOF

3. Cluster-Based Local Outlier Factor (CBLOF)

• Paper: Discovering cluster-based local outliers, https://www.sciencedirect.com/science/article/abs/pii/S0167865503000035
• Implementation: github https://github.com/yzhao062/pyod/blob/master/pyod/models/cblof.py

4. Exact-Storm

• Paper: Detecting Distance-Based Outliers in Streams of Data, http://delab.csd.auth.gr/~papajim/presentations/files/angiulliCIKM2007Detecting.pdf
• Implementation: https://infolab.usc.edu/Luan/Outlier/CountBasedWindow/DODDS/src/outlierdetection/

5. Approx-Storm

• Paper: Detecting Distance-Based Outliers in Streams of Data, http://delab.csd.auth.gr/~papajim/presentations/files/angiulliCIKM2007Detecting.pdf
• Implementation: https://infolab.usc.edu/Luan/Outlier/CountBasedWindow/DODDS/src/outlierdetection/

6. Abstract-C

• Paper: Neighbor-based pattern detection for windows over streaming data, https://dl.acm.org/doi/10.1145/1516360.1516422
• Implementation:

7. Direct Update of Events - DUE

• Paper: Continuous monitoring of distance-based outliers over data streams, https://ieeexplore.ieee.org/document/5767923
• Implementation: https://infolab.usc.edu/Luan/Outlier/CountBasedWindow/DODDS/src/outlierdetection/

8. Micro-Cluster Based Algorithm - MCOD

• Paper: Continuous monitoring of distance-based outliers over data streams, https://ieeexplore.ieee.org/document/5767923
• Implementation: https://infolab.usc.edu/Luan/Outlier/CountBasedWindow/DODDS/src/outlierdetection/

9. Thresh LEAP

• Paper: Scalable distance-based outlier detection over high-volume data streams, http://people.csail.mit.edu/lcao/papers/icde14.pdf
• Implementation: https://infolab.usc.edu/Luan/Outlier/CountBasedWindow/DODDS/src/outlierdetection/

Clustering-based anomaly detection methods

1. DenStream

• Paper: Density-Based Clustering over an Evolving Data Stream with Noise, https://archive.siam.org/meetings/sdm06/proceedings/030caof.pdf
• Implementation: github https://github.com/issamemari/DenStream

2. D-Stream

• Paper: Density-based clustering for real-time stream data, https://dl.acm.org/doi/10.1145/1281192.1281210
• Implementation: https://github.com/richard-moulton/D-Stream

3. C-DenStream

• Paper: C-DenStream: Using Domain Knowledge on a Data Stream, https://link.springer.com/chapter/10.1007/978-3-642-04747-3_23
• Implementation: https://github.com/MaLL-UFSCar/CDenStream

4. ClusTree

• Paper: Clustering trees: a visualization for evaluating clusterings at multiple resolutions, http://dx.doi.org/10.1093/gigascience/giy083
• Implementation: github https://github.com/lazappi/clustree

5. MR-Stream

• Paper: Density-based clustering of data streams at multiple resolutions, https://dl.acm.org/doi/10.1145/1552303.1552307
• Implementation: github

6. SOStream

• Paper: SOStream: Self Organizing Density-Based Clustering over Data Stream, https://link.springer.com/chapter/10.1007/978-3-642-31537-4_21
• Implementation: github

7. CluStream

• Paper: A Framework for Clustering Evolving Data Streams, https://dl.acm.org/doi/10.5555/1315451.1315460
• Implementation: github https://github.com/narjes23/Clustream-algorithm

One classification based anomaly detection

1. OCSVM

• Paper: Enhancing one-class support vector machines for unsupervised anomaly detection, https://dl.acm.org/doi/10.1145/2500853.2500857
• Implementation: sklearn https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html

2. Deep One-Class Classification

• Paper: Deep One-Class Classification, http://data.bit.uni-bonn.de/publications/ICML2018.pdf Implementation: github https://github.com/lukasruff/Deep-SVDD-PyTorch

Tree based anomaly detection

1. Isolation Forest

• Paper: Isolation-Based Anomaly Detection, https://dl.acm.org/doi/10.1145/2133360.2133363
• Implementation: sklearn https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html

2. Extended Isolation Forest

• Paper: Extended Isolation Forest, https://arxiv.org/abs/1811.02141
• Implementation: github https://github.com/sahandha/eif

3. Robust Random Cut Forest (RRCF)

• Paper: Robust Random Cut Forest Based Anomaly Detection On Streams, http://proceedings.mlr.press/v48/guha16.pdf
• Implementation: github https://github.com/kLabUM/rrcf

4. Streaming HS-Tree

• Paper: Fast Anomaly Detection for Streaming Data, https://www.ijcai.org/Proceedings/11/Papers/254.pdf
• Implementation: github https://github.com/yli96/HSTree

5. RS-Forest

• Paper: RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection, https://ieeexplore.ieee.org/document/7023377
• Implementation: github https://github.com/shubhomoydas/pyaad