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Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point.
Detecting Intrusions using System Calls: Alternative Data Models. In
Christina Warrender, Stephanie Forrest, and Barak Pearlmutter. 1999 · 1999
Earlier work this paper cites.
Anomaly Detection: A Survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009 · 2009
Earlier work this paper cites.
Outlier Analysis
Charu C. Aggarwal. 2013 · 2013
Earlier work this paper cites.
Machine Learning Applications in Cancer Prognosis and Prediction
Konstantina Kourou, Themis P. Exarchos, Konstantinos P. Exarchos, Michalis V. Karamouzis, and Dimitrios I. Fotiadis. 2015 · 2015
Earlier work this paper cites.
Evaluating Real-Time Anomaly Detection Algorithms - The Numenta Anomaly Benchmark. In IEEE International Conference on Machine Learning and Applications (ICMLA) . 38–44
Alexander Lavin and Subutai Ahmad. 2015 · 2015
Cited alongside, same era.
Long Short Term Memory Networks for Anomaly Detection in Time Series. In
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015 · 2015
Cited alongside, same era.
AnomalyDetection R Package
Twitter. 2015 · 2015
Cited alongside, same era.
Robust Random Cut Forest Based Anomaly Detection on Streams. In International Conference on Machine Learning (ICML) . 2712–2721
Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers. 2016 · 2016
Cited alongside, same era.
Unsupervised Real-time Anomaly Detection for Streaming Data
Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha. 2017 · 2017
Later among the works it cites.
Demystifying Numenta Anomaly Benchmark. In
Nidhi Singh and Craig Olinsky. 2017 · 2017
Later among the works it cites.
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, and Stan Zdonik. 2018 · 2018
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