Next Article in Journal
A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors
Previous Article in Journal
A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time
Previous Article in Special Issue
Study on Performance Evaluation and Prediction of Francis Turbine Units Considering Low-Quality Data and Variable Operating Conditions
Article

Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System

School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
*
Author to whom correspondence should be addressed.
Academic Editor: Emanuele Carpanzano
Received: 31 March 2022 / Revised: 5 May 2022 / Accepted: 13 May 2022 / Published: 15 May 2022
(This article belongs to the Special Issue Advancing Reliability & Prognostics and Health Management)
As a core tool, anomaly detection based on a generative adversarial network (GAN) is showing its powerful potential in protecting the safe and stable operation of industrial control systems (ICS) under the Internet of Things (IoT). However, due to the long-tailed distribution of operating data in ICS, existing GAN-based anomaly detection models are prone to misjudging an unseen marginal sample as an outlier. Moreover, it is difficult to collect abnormal samples from ICS. To solve these challenges, a dual auto-encoder GAN-based anomaly detection model is proposed for the industrial control system, simply called the DAGAN model, to achieve an accurate and efficient anomaly detection without any abnormal sample. First, an “encoder–decoder–encoder” architecture is used to build a dual GAN model for learning the latent data distribution without any anomalous sample. Then, a parameter-free dynamic strategy is proposed to robustly and accurately learn the marginal distribution of the training data through dynamic interaction between two GANs. Finally, based on the learned normal distribution and marginal distribution, an optimized anomaly score is used to measure whether a sample is an outlier, thereby reducing the probability of a marginal sample being misjudged. Extensive experiments on multiple datasets demonstrate the advantages of our DAGAN model. View Full-Text
Keywords: anomaly detection; dual GAN; auto-encoder; industrial control system anomaly detection; dual GAN; auto-encoder; industrial control system
Show Figures

Figure 1

MDPI and ACS Style

Chen, L.; Li, Y.; Deng, X.; Liu, Z.; Lv, M.; Zhang, H. Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System. Appl. Sci. 2022, 12, 4986. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104986

AMA Style

Chen L, Li Y, Deng X, Liu Z, Lv M, Zhang H. Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System. Applied Sciences. 2022; 12(10):4986. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104986

Chicago/Turabian Style

Chen, Lei, Yuan Li, Xingye Deng, Zhaohua Liu, Mingyang Lv, and Hongqiang Zhang. 2022. "Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System" Applied Sciences 12, no. 10: 4986. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104986

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop