Network data analysis and anomaly detection using CNN technique for industrial control systems security

Yibo Hu; DInghua Zhang; Guoyan Cao; Quan Pan

doi:10.1109/SMC.2019.8913895

Network data analysis and anomaly detection using CNN technique for industrial control systems security

Yibo Hu, DInghua Zhang, Guoyan Cao, Quan Pan

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.

Original language	English
Title of host publication	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	593-597
Number of pages	5
ISBN (Electronic)	9781728145693
DOIs	https://doi.org/10.1109/SMC.2019.8913895
State	Published - Oct 2019
Event	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy Duration: 6 Oct 2019 → 9 Oct 2019

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume	2019-October
ISSN (Print)	1062-922X

Conference

Conference	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Country/Territory	Italy
City	Bari
Period	6/10/19 → 9/10/19

Access to Document

10.1109/SMC.2019.8913895

Cite this

Hu, Y., Zhang, DI., Cao, G., & Pan, Q. (2019). Network data analysis and anomaly detection using CNN technique for industrial control systems security. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 (pp. 593-597). Article 8913895 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2019.8913895

Hu, Yibo ; Zhang, DInghua ; Cao, Guoyan et al. / Network data analysis and anomaly detection using CNN technique for industrial control systems security. 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 593-597 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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title = "Network data analysis and anomaly detection using CNN technique for industrial control systems security",

abstract = "Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.",

author = "Yibo Hu and DInghua Zhang and Guoyan Cao and Quan Pan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 ; Conference date: 06-10-2019 Through 09-10-2019",

year = "2019",

month = oct,

doi = "10.1109/SMC.2019.8913895",

language = "英语",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "593--597",

booktitle = "2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019",

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Hu, Y, Zhang, DI, Cao, G & Pan, Q 2019, Network data analysis and anomaly detection using CNN technique for industrial control systems security. in 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019., 8913895, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 593-597, 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019, Bari, Italy, 6/10/19. https://doi.org/10.1109/SMC.2019.8913895

Network data analysis and anomaly detection using CNN technique for industrial control systems security. / Hu, Yibo; Zhang, DInghua; Cao, Guoyan et al.
2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 593-597 8913895 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hu, Yibo

AU - Zhang, DInghua

AU - Cao, Guoyan

AU - Pan, Quan

PY - 2019/10

Y1 - 2019/10

N2 - Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.

AB - Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.

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U2 - 10.1109/SMC.2019.8913895

DO - 10.1109/SMC.2019.8913895

M3 - 会议稿件

AN - SCOPUS:85076725234

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 593

EP - 597

BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019

Y2 - 6 October 2019 through 9 October 2019

ER -

Hu Y, Zhang DI, Cao G, Pan Q. Network data analysis and anomaly detection using CNN technique for industrial control systems security. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 593-597. 8913895. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC.2019.8913895

Network data analysis and anomaly detection using CNN technique for industrial control systems security

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