I forked this repo to have list of options for stall-based anomoly detection.

• Isolation Forest - ICDM 2008.

  • Implemented

• LOF: Identifying Density-Based Local Outliers - SIGMOD 2000.

  • Implemented

• Extended Isolation Forest

  • Implemented

• Robust Random Cut Forest Based Anomaly Detection On Streams

  • Same as EIF?

• Support Vector Method for Novelty Detection - NIPS 2000

  • Implemented

• One-Class SVMs for Document Classification - JMLR 2001.

  • Implemented

• Support Vector Data Description

• Can I Trust My One-Class Classification?

• Efficient Anomaly Detection via Matrix Sketching - NIPS 2018

  • useful for the anomalous command project for Brian?
  • No Arxiv code implementation

• robust deep and inductive anomaly detection - ECML PKDD 2017

  • Has promise.
  • Code provided (https://github.com/raghavchalapathy/rcae)
  • Test cases are images, so we need to adapt for vector/scalar inputs.

• A loss framework for calibrated anomaly detection - NIPS 2018

• A Practical Algorithm for Distributed Clustering and Outlier Detection - NIPS 2018

• Detecting Multiple Periods and Periodic Patterns in Event Time Sequences - CIKM 2017.

• ranking causal anomalies via temporal and dynamical analysis on vanishing correlations - KDD 2016.
  • Works on Graph data, not scalar.

• MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams - AAAI 2020.
  • We do not have graph-data.

• Variational Autoencoder based Anomaly Detection using Reconstruction Probability

• [https://github.com/yuxiao-ash/ITAE-Pytorch-Anomaly_Detection]

  • uses transformers

• Learning sparse representation with variational auto-encoder for anomaly detection

  • The data they use are Graph, Image, and Video data, not scalar sequences.
  • No code?

• Anomaly Detection with Robust Deep Autoencoders - KDD 2017.

• DEEP AUTOENCODING GAUSSIAN MIXTURE MODEL FOR UNSUPERVISED ANOMALY DETECTION - ICLR 2018.

• Generative Probabilistic Novelty Detection with Adversarial Autoencoders - NIPS 2018

• Multidimensional Time Series Anomaly Detection: A GRU-based Gaussian Mixture Variational Autoencoder Approach - ACML 2018

  • No code?

• A Multimodel Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder - IEEE Robotics and Automation Letters 2018.

• Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery - IPMI 2017.

• Efficient-GAN-Based Anomaly Detection ICLR Workshop 2018.

• Anomaly detection with generative adversarial networks - Reject by ICLR 2018, but was used as baseline method in recent published NIPS paper.

• Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning - CIKM 2017.

• Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks - IJCAI 2018.

• High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning - Pattern Recognition 2018.

• Optimal single-class classification strategies - NIPS 2007.

• Deep One-Class Classification - ICML 2018.

• Deep Semi-Supervised Anomaly Detection - ICLR 2020.

  • TE: 5/25 Try this one first !

• Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification - IJCNN 2021

• Explainable Deep One-Class Classification ICLR 2021.

• Learning and Evaluating Representation for Deep One-Class Classification ICLR 2021.

• Deep structured energy based models for anomaly detection - ICML 2016

• A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection - AAAI 2013

• Stochastic Online Anomaly Analysis for Streaming Time Series - IJCAI 2017

• Long short term memory networks for anmomaly detection in time series

• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection - ICML 2016 Workshop.

• Contextual Outlier Interpretation -IJCAI 2018

• Precision and Recall for Time Series - NIPS 2018

• Deep Anomaly Detection Using Geometric Transformations - NIPS 2018

• Incorporating Feedback into Tree-based Anomaly Detection - KDD 2017 Workshop on Interactive Data Exploration and Analytics.

• Feedback-Guided Anomaly Discovery via Online Optimization - KDD 2018.

• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications - WWW 2018.
• Unsupervised Online Anomaly Detection with Parameter Adaptation for KPI Abrupt Changes - TNSM 2019.

• Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs -IJCAI 2019
• Robust log-based anomaly detection on unstable log data - FSE 2019
• Prefix: Switch failure prediction in datacenter networks -SIGMETRICS 2018
• DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning - CCS 2017
• Mining Invariants from Logs for System Problem Detection - USENIX 2010

• Anomaly detection in dynamic networks: a survey

  • We do not have graph data

• Anomaly Detection : A Survey

  • Rather old info. Specifically the Neural approaches.

• A Survey of Recent Trends in One Class Classification

  • Old. + Springer paywall.

• A survey on unsupervised outlier detection in high‐dimensional numerical data

  • Wiley Paywall