Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems

Yan Du, Yuanyuan Huang (), Guogen Wan and Peilin He
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Yan Du: Department of Network Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Yuanyuan Huang: Department of Network Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Guogen Wan: Department of Network Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Peilin He: Department of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, PA 15260, USA

Mathematics, 2022, vol. 10, issue 22, 1-20

Abstract: In this paper, we propose an unsupervised anomaly detection method based on the Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative Adversarial Network (GAN) to detect anomalies in industrial control system (ICS) using cyber–physical fusion features. This method improves the recall of anomaly detection and overcomes the challenges of unbalanced datasets and insufficient labeled samples in ICS. As a first step, additional network features are extracted and fused with physical features to create a cyber–physical dataset. Following this, the model is trained using normal data to ensure that it can properly reconstruct the normal data. In the testing phase, samples with unknown labels are used as inputs to the model. The model will output an anomaly score for each sample, and whether a sample is anomalous depends on whether the anomaly score exceeds the threshold. Whether using supervised or unsupervised algorithms, experimentation has shown that (1) cyber–physical fusion features can significantly improve the performance of anomaly detection algorithms; (2) the proposed method outperforms several other unsupervised anomaly detection methods in terms of accuracy, recall, and F1 score; (3) the proposed method can detect the majority of anomalous events with a low false negative rate.

Keywords: deep learning; anomaly detection; cyber–physical; industrial control systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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