Real-Time Adaptive Anomaly Detection in Industrial IoT Environments

TL;DR

This paper introduces an adaptive anomaly detection method for industrial IoT data streams that effectively handles multi-dimensional, dynamic data, improving accuracy and scalability over existing approaches.

Contribution

It presents a novel anomaly detection algorithm combining multi-source prediction and concept drift adaptation tailored for IIoT environments.

Findings

Achieves up to 89.71% AUC accuracy

Outperforms state-of-the-art methods in detection accuracy

Demonstrates improved scalability and efficiency

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.