awesome-anomaly-detection
github.com/xmoanvaf/awesome-anomaly-detection ↗A complete list of papers on anomaly detection.
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Classical MethodDeep Learning MethodAnomaly Detection ApplicationsSurvey
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Deep Learning Method
- A Generalized Student-t Based Approach to Mixed-Type Anomaly DetectionTime series
AAAI 2013
- A Multimodel Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational AutoencoderGenerative Methods
IEEE Robotics and Automation Letters 2018.
- Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere LearningHypersphereical Learning
CIKM 2017.
- Anomaly detection with generative adversarial networksGenerative Methods
Reject by ICLR 2018, but was used as baseline method in recent published NIPS paper.
- Anomaly Detection with Robust Deep AutoencodersGenerative Methods
KDD 2017.
- Contextual Outlier InterpretationInterpretation
Classical Method
- A loss framework for calibrated anomaly detectionPCA-based
NIPS 2018
- A Practical Algorithm for Distributed Clustering and Outlier DetectionClustering
NIPS 2018
- Can I Trust My One-Class Classification?One-Class Classification
- Detecting Multiple Periods and Periodic Patterns in Event Time SequencesCorrelation
CIKM 2017.
- Efficient Anomaly Detection via Matrix SketchingOne-Class Classification
NIPS 2018
- Extended Isolation Forest
Survey
Anomaly Detection Applications
- DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep LearningLog
CCS 2017
- Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logsLog
- Mining Invariants from Logs for System Problem DetectionLog
USENIX 2010
- Prefix: Switch failure prediction in datacenter networksLog
- Robust log-based anomaly detection on unstable log dataLog
FSE 2019
- Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web ApplicationsKPI
WWW 2018.
Showing a sample of 55 resources. View the full list on GitHub →