Machine-Learning-Based Anomaly Detection for GOOSE in Digital Substations

Hong Nhung-Nguyen, Mansi Girdhar, Yong-Hwa Kim () and Junho Hong
Additional contact information
Hong Nhung-Nguyen: Department of AI and Software Enineering, School of Computing, Gachon Unviersity, Seongnam-si 1342, Gyeonggi-do, Republic of Korea
Mansi Girdhar: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Yong-Hwa Kim: Department of Artificial Intelligence, Korea National University of Transportation, Uiwang-si 16106, Gyeonggi-do, Republic of Korea
Junho Hong: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA

Energies, 2024, vol. 17, issue 15, 1-20

Abstract: Digital substations have adopted a high amount of information and communication technology (ICT) and cyber–physical systems (CPSs) for monitoring and control. As a result, cyber attacks on substations have been increasing and have become a major concern. An intrusion-detection system (IDS) could be a solution to detect and identify the abnormal behaviors of hackers. In this paper, a Deep Neural Network (DNN)-based IDS is proposed to detect malicious generic object-oriented substation event (GOOSE) communication over the process and station bus network, followed by the multiclassification of the cyber attacks. For training, both the abnormal and the normal substation networks are monitored, captured, and logged, and then the proposed algorithm is applied for distinguishing normal events from abnormal ones within the network communication packets. The designed system is implemented and tested with a real-time IEC 61850 GOOSE message dataset using two different approaches. The experimental results show that the proposed system can successfully detect intrusions with an accuracy of 98%. In addition, a comparison is performed in which the proposed IDS outperforms the support vector machine (SVM)-based IDS.

Keywords: deep neural networks; power systems fault classification; fault line location identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/15/3745/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/15/3745/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:15:p:3745-:d:1445442

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().