针对工业物联网实时异常检测难题，提出自适应多源预测方法。
Real-Time Adaptive Anomaly Detection in Industrial IoT Environments
- 融合多源预测与概念漂移适应机制，动态响应数据变化。
- 在真实数据流上实现最高89.71%的AUC准确率，满足实时性要求。
- 适合需要高可靠性的工业系统运维人员和自动化检测开发者。
为保障下一代网络的可靠性与服务可用性，新一代系统需依赖由先进机器学习方法驱动的自动化异常检测体系，以处理多维异构数据。此类多维异构数据主要出现在当前的工业互联网（IIoT）环境中，实时异常检测对预防故障和及时处置至关重要。然而，现有异常检测方法难以有效应对IIoT中多维数据流的复杂性与动态性。本文提出一种基于多源预测模型与概念漂移适应机制的自适应异常检测方法。该算法将预测模型与新型漂移适应方法融合，实现高精度、高效率的异常检测，并具备良好可扩展性。基于实际数据流的评估表明，所提方法在性能上优于现有最先进方法，最大达到89.71%的AUC准确率，同时满足给定的效率与可扩展性要求。
原文摘要 · Abstract (English)
To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in today's industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by achieving up to an 89.71% accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements.
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