A Gated Recurrent Unit based Intrusion Detection for SCADA Networks

Mambwe Sydney Kasongo; Yanxia Sun

doi:10.1109/ICCCS51487.2021.9776331

A Gated Recurrent Unit based Intrusion Detection for SCADA Networks

Mambwe Sydney Kasongo, Yanxia Sun

Electrical and Electronic Engineering Science

University of Johannesburg

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

Abstract

Industrial control systems are rapidly evolving and they process large volumes of critical information that flow through them. Moreover, the development and advances of various industrial communication systems and the ascent of Internet connectivity have caused a surge in new types of threats and intrusions. In this study we implement a Gated Recurrent Unit (GRU) Intrusion Detection System (IDS) destined to secure Supervisory Control and Data Acquisition (SCADA) networks. The GRU algorithm used in this research is coupled to the Information Gain (IG) approach for feature selection. The NSL-KDD dataset was employed to assess the performance of the IG-GRU-IDS. The results demonstrated that with only 20 attributes of the NSL-KDD, the IG-GRU-IDS achieved a validation accuracy of 99.52%, a test accuracy of 87.49% and a F-measure of 99.51 %. These results were superior to those obtained by Simple RNNs and LSTM based RNNs.

Original language	English
Title of host publication	Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665440394
DOIs	https://doi.org/10.1109/ICCCS51487.2021.9776331
Publication status	Published - 2021
Event	6th International Conference on Computing, Communication and Security, ICCCS 2021 - Las Vegas, United States Duration: 4 Oct 2021 → 6 Oct 2021

Publication series

Name	Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021

Conference

Conference	6th International Conference on Computing, Communication and Security, ICCCS 2021
Country/Territory	United States
City	Las Vegas
Period	4/10/21 → 6/10/21

Keywords

Deep Learning
Machine Learning
Network Security
SCADA

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality
Communication

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICCCS51487.2021.9776331

Cite this

Kasongo, M. S., & Sun, Y. (2021). A Gated Recurrent Unit based Intrusion Detection for SCADA Networks. In Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021 (Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCS51487.2021.9776331

Kasongo, Mambwe Sydney ; Sun, Yanxia. / A Gated Recurrent Unit based Intrusion Detection for SCADA Networks. Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021).

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title = "A Gated Recurrent Unit based Intrusion Detection for SCADA Networks",

abstract = "Industrial control systems are rapidly evolving and they process large volumes of critical information that flow through them. Moreover, the development and advances of various industrial communication systems and the ascent of Internet connectivity have caused a surge in new types of threats and intrusions. In this study we implement a Gated Recurrent Unit (GRU) Intrusion Detection System (IDS) destined to secure Supervisory Control and Data Acquisition (SCADA) networks. The GRU algorithm used in this research is coupled to the Information Gain (IG) approach for feature selection. The NSL-KDD dataset was employed to assess the performance of the IG-GRU-IDS. The results demonstrated that with only 20 attributes of the NSL-KDD, the IG-GRU-IDS achieved a validation accuracy of 99.52%, a test accuracy of 87.49% and a F-measure of 99.51 %. These results were superior to those obtained by Simple RNNs and LSTM based RNNs.",

keywords = "Deep Learning, Machine Learning, Network Security, SCADA",

author = "Kasongo, {Mambwe Sydney} and Yanxia Sun",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 6th International Conference on Computing, Communication and Security, ICCCS 2021 ; Conference date: 04-10-2021 Through 06-10-2021",

year = "2021",

doi = "10.1109/ICCCS51487.2021.9776331",

language = "English",

series = "Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021",

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

booktitle = "Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021",

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Kasongo, MS & Sun, Y 2021, A Gated Recurrent Unit based Intrusion Detection for SCADA Networks. in Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021. Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021, Institute of Electrical and Electronics Engineers Inc., 6th International Conference on Computing, Communication and Security, ICCCS 2021, Las Vegas, United States, 4/10/21. https://doi.org/10.1109/ICCCS51487.2021.9776331

A Gated Recurrent Unit based Intrusion Detection for SCADA Networks. / Kasongo, Mambwe Sydney; Sun, Yanxia.
Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021).

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

TY - GEN

T1 - A Gated Recurrent Unit based Intrusion Detection for SCADA Networks

AU - Kasongo, Mambwe Sydney

AU - Sun, Yanxia

PY - 2021

Y1 - 2021

N2 - Industrial control systems are rapidly evolving and they process large volumes of critical information that flow through them. Moreover, the development and advances of various industrial communication systems and the ascent of Internet connectivity have caused a surge in new types of threats and intrusions. In this study we implement a Gated Recurrent Unit (GRU) Intrusion Detection System (IDS) destined to secure Supervisory Control and Data Acquisition (SCADA) networks. The GRU algorithm used in this research is coupled to the Information Gain (IG) approach for feature selection. The NSL-KDD dataset was employed to assess the performance of the IG-GRU-IDS. The results demonstrated that with only 20 attributes of the NSL-KDD, the IG-GRU-IDS achieved a validation accuracy of 99.52%, a test accuracy of 87.49% and a F-measure of 99.51 %. These results were superior to those obtained by Simple RNNs and LSTM based RNNs.

AB - Industrial control systems are rapidly evolving and they process large volumes of critical information that flow through them. Moreover, the development and advances of various industrial communication systems and the ascent of Internet connectivity have caused a surge in new types of threats and intrusions. In this study we implement a Gated Recurrent Unit (GRU) Intrusion Detection System (IDS) destined to secure Supervisory Control and Data Acquisition (SCADA) networks. The GRU algorithm used in this research is coupled to the Information Gain (IG) approach for feature selection. The NSL-KDD dataset was employed to assess the performance of the IG-GRU-IDS. The results demonstrated that with only 20 attributes of the NSL-KDD, the IG-GRU-IDS achieved a validation accuracy of 99.52%, a test accuracy of 87.49% and a F-measure of 99.51 %. These results were superior to those obtained by Simple RNNs and LSTM based RNNs.

KW - Deep Learning

KW - Machine Learning

KW - Network Security

KW - SCADA

UR - http://www.scopus.com/inward/record.url?scp=85131878215&partnerID=8YFLogxK

U2 - 10.1109/ICCCS51487.2021.9776331

DO - 10.1109/ICCCS51487.2021.9776331

M3 - Conference contribution

AN - SCOPUS:85131878215

T3 - Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021

BT - Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Computing, Communication and Security, ICCCS 2021

Y2 - 4 October 2021 through 6 October 2021

ER -

Kasongo MS, Sun Y. A Gated Recurrent Unit based Intrusion Detection for SCADA Networks. In Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 6th International Conference on Computing, Communication and Security, ICCCS 2021). doi: 10.1109/ICCCS51487.2021.9776331

A Gated Recurrent Unit based Intrusion Detection for SCADA Networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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