Real-Time Adaptive Anomaly Detection in Industrial IoT Environments

2024-12-01

IEEE Transactions on Network and Service Management

AI Summary

This work addresses the significant challenges posed by the dynamic and complex nature of high-dimensional heterogeneous data streams in Industrial Internet of Things (IIoT) environments for real-time anomaly detection. To this end, we propose a novel detection approach that integrates multi-source predictive modeling with an adaptive mechanism for concept drift. By continuously identifying distributional shifts and dynamically updating the underlying model, the method substantially enhances detection accuracy and robustness. Experimental evaluation on real-world IIoT datasets demonstrates that the proposed approach achieves an AUC of 89.71%, significantly outperforming current state-of-the-art methods while maintaining strong real-time performance, scalability, and computational efficiency.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Industrial IoT
• anomaly detection
• multi-dimensional data
• real-time
• concept drift

Innovation

• adaptive anomaly detection
• multi-source prediction
• concept drift adaptation
• Industrial IoT
• real-time streaming data