  Real-time analytics and decision-making require online anomaly detection
(OAD) to handle drifts in data streams efficiently and effectively.
Unfortunately, existing approaches are often constrained by their limited
detection capacity and slow adaptation to evolving data streams, inhibiting
their efficacy and efficiency in handling concept drift, which is a major
challenge in evolving data streams. In this paper, we introduce METER, a novel
dynamic concept adaptation framework that introduces a new paradigm for OAD.
METER addresses concept drift by first training a base detection model on
historical data to capture recurring central concepts, and then learning to
dynamically adapt to new concepts in data streams upon detecting concept drift.
Particularly, METER employs a novel dynamic concept adaptation technique that
leverages a hypernetwork to dynamically generate the parameter shift of the
base detection model, providing a more effective and efficient solution than
conventional retraining or fine-tuning approaches. Further, METER incorporates
a lightweight drift detection controller, underpinned by evidential deep
learning, to support robust and interpretable concept drift detection. We
conduct an extensive experimental evaluation, and the results show that METER
significantly outperforms existing OAD approaches in various application
scenarios.
