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.

翻译：实时分析与决策需要在线异常检测（OAD）高效且有效地处理数据流中的概念漂移问题。然而，现有方法通常受限于其有限的检测能力以及对演化数据流的缓慢适应，这阻碍了它们在处理概念漂移（演化数据流的主要挑战）时的效能与效率。本文提出METER——一种新颖的动态概念自适应框架，为OAD引入全新范式。METER通过以下方式应对概念漂移：首先在历史数据上训练基础检测模型以捕获重复出现的核心概念，随后在检测到概念漂移时学习动态适应数据流中的新概念。特别地，METER采用新型动态概念自适应技术，利用超网络动态生成基础检测模型的参数偏移，相比传统重训练或微调方法提供了更高效且有效的解决方案。此外，METER集成基于证据深度学习的轻量级漂移检测控制器，以支持鲁棒且可解释的概念漂移检测。我们开展了广泛的实验评估，结果表明METER在各种应用场景中显著优于现有OAD方法。