Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network
Joint Authors
Wang, Bailing
Liu, Hongri
Qu, Haikuo
Wang, Chao
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency.
The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT.
ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives.
ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years.
The risk of being attacked increases as a result.
In order to protect these assets, intrusion detection systems (IDS) have drawn much attention.
As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS.
In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed.
Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone.
With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack.
In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset.
The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.
American Psychological Association (APA)
Wang, Chao& Wang, Bailing& Liu, Hongri& Qu, Haikuo. 2020. Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1214941
Modern Language Association (MLA)
Wang, Chao…[et al.]. Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1214941
American Medical Association (AMA)
Wang, Chao& Wang, Bailing& Liu, Hongri& Qu, Haikuo. Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1214941
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214941