Handling adversarial concept drift in streaming data

作者：

Highlights：

• Analyzing characteristics of adversarial drift, as a special type of concept drift.

• The Predict-Detect framework takes preemptive steps to benefit dynamic drift handling.

• Adversarial drifts are detected from unlabeled data, with high reliability.

• Feature honeypots capture adversarial class samples, for learning on imbalanced streams.

• A novel simulation framework, for generating adversarial drift on real world datasets.

摘要

•Analyzing characteristics of adversarial drift, as a special type of concept drift.•The Predict-Detect framework takes preemptive steps to benefit dynamic drift handling.•Adversarial drifts are detected from unlabeled data, with high reliability.•Feature honeypots capture adversarial class samples, for learning on imbalanced streams.•A novel simulation framework, for generating adversarial drift on real world datasets.

论文关键词：Adversarial machine learning,Concept drift,Streaming data,Limited labeling,Active learning,Classification

论文评审过程：Received 16 July 2017, Revised 10 December 2017, Accepted 11 December 2017, Available online 11 December 2017, Version of Record 19 December 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.12.022