No free lunch but a cheaper supper: A general framework for streaming anomaly detection

作者：

Highlights：

• We postulate the problem of unifying streaming anomaly detection.

• We propose a meta-framework for a flexible and adaptable anomaly detection procedure.

• Our framework helps to overcome the limitations of one-size-fitsall solutions.

• We propose a novel anomaly-aware reservoir sampling scheme.

• We conduct an extensive comparison study on 20 detectors using various datasets.

摘要

•We postulate the problem of unifying streaming anomaly detection.•We propose a meta-framework for a flexible and adaptable anomaly detection procedure.•Our framework helps to overcome the limitations of one-size-fitsall solutions.•We propose a novel anomaly-aware reservoir sampling scheme.•We conduct an extensive comparison study on 20 detectors using various datasets.

论文关键词：Anomaly detection,Stream mining,Reservoir sampling,Online learning

论文评审过程：Received 21 September 2019, Revised 10 April 2020, Accepted 12 April 2020, Available online 14 April 2020, Version of Record 4 May 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113453