AD-FIT: industrial anomaly detection via fusion of IoT sensing and network traffic data

Abstract

Anomaly detection is an important research topic in the Industrial Internet of Things (IIoT). In recent years, deep learning has been exploited to analyze complex IIoT data and build anomaly detection models. Due to the lack of abnormal samples and the difficulty of labeling industrial data, unsupervised deep learning has become the mainstream technique for IIoT anomaly detection, with autoencoders being the most representative approaches. However, the existing autoencoder-based IIoT anomaly detection models predominantly focus on a single data modality, since existing data fusion frameworks are mostly designed for supervised learning tasks, while it is infeasible to simultaneously reconstruct heterogeneous data modalities in a unified autoencoder. To address this limitation, this paper focuses on IoT sensing data and network traffic data and proposes AD-FIT, a novel autoencoder framework for IIoT anomaly detection via the fusion of IoT sensing and network traffic data in an unsupervised manner. Specifically, it creates multiple local autoencoders with different architectures to fit the two data modalities, and then fuses their reconstruction errors through a global autoencoder. We conducted extensive experiments based on a public IIoT dataset. Experimental results show that AD-FIT achieves the best overall anomaly detection performance among the evaluated baseline methods, with F1-score improvements ranging from 7.1% to 38.6%.