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research-article

METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

Authors:

Wenqiao ZhangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 17, Issue 4

Pages 794 - 807

https://doi.org/10.14778/3636218.3636233

Published: 05 March 2024 Publication History

Abstract

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.

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METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 17, Issue 4

December 2023

309 pages

ISSN:2150-8097

Editors:
Meihui Zhang
Beijing Institute of Technology
,
Cyrus Shahabi
University of Southern California

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Published: 05 March 2024

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Cited By

Zheng HZhao JZhu JYe ZDeng F(2025)Long-term urban air quality prediction with hierarchical attention loop networkSustainable Cities and Society10.1016/j.scs.2024.106010118(106010)Online publication date: Jan-2025
https://doi.org/10.1016/j.scs.2024.106010
Zhu JCai SDeng FOoi BWu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly DetectionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681190(48-57)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681190
Yatagha RNebebe BWaedt KRuland CSerra ESpezzano F(2024)Towards a Zero-Day Anomaly Detector in Cyber Physical Systems Using a Hybrid VAE-LSTM-OCSVM ModelProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680064(5038-5045)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680064
Cao YMa YZhu YTing K(2024)Revisiting streaming anomaly detection: benchmark and evaluationArtificial Intelligence Review10.1007/s10462-024-10995-w58:1Online publication date: 7-Nov-2024
https://doi.org/10.1007/s10462-024-10995-w

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