An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks

Meng Zhang; Shan Fu; Jun Yan; Huiyan Zhang; Chenhao Ling; Chao Shen; Peng Shi

doi:10.1109/SMC52423.2021.9658797

An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks

Meng Zhang
, Shan Fu
, Jun Yan
, Huiyan Zhang
, Chenhao Ling
, Chao Shen
, Peng Shi

Faculty of Electronic and Information Engineering

China Academy of Information and Communications Technology
Concordia University
Chongqing Technology and Business University
Xi'an Jiaotong University
University of Adelaide

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

5 Scopus citations

Abstract

In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.

Original language	English
Title of host publication	2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	921-926
Number of pages	6
ISBN (Electronic)	9781665442077
DOIs	https://doi.org/10.1109/SMC52423.2021.9658797
State	Published - 2021
Event	2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia Duration: 17 Oct 2021 → 20 Oct 2021

Publication series

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

Conference

Conference	2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/Territory	Australia
City	Melbourne
Period	17/10/21 → 20/10/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/SMC52423.2021.9658797

Cite this

Zhang, M., Fu, S., Yan, J., Zhang, H., Ling, C., Shen, C., & Shi, P. (2021). An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks. In 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 (pp. 921-926). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC52423.2021.9658797

Zhang, Meng ; Fu, Shan ; Yan, Jun et al. / An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks. 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 921-926 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

@inproceedings{784e92aefc4b4f259619c23fbc15f419,

title = "An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks",

abstract = "In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.",

author = "Meng Zhang and Shan Fu and Jun Yan and Huiyan Zhang and Chenhao Ling and Chao Shen and Peng Shi",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 ; Conference date: 17-10-2021 Through 20-10-2021",

year = "2021",

doi = "10.1109/SMC52423.2021.9658797",

language = "英语",

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

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

pages = "921--926",

booktitle = "2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021",

}

Zhang, M, Fu, S, Yan, J, Zhang, H, Ling, C, Shen, C & Shi, P 2021, An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks. in 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Institute of Electrical and Electronics Engineers Inc., pp. 921-926, 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021, Melbourne, Australia, 17/10/21. https://doi.org/10.1109/SMC52423.2021.9658797

An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks. / Zhang, Meng; Fu, Shan; Yan, Jun et al.
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 921-926 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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

TY - GEN

T1 - An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks

AU - Zhang, Meng

AU - Fu, Shan

AU - Yan, Jun

AU - Zhang, Huiyan

AU - Ling, Chenhao

AU - Shen, Chao

AU - Shi, Peng

PY - 2021

Y1 - 2021

N2 - In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.

AB - In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.

UR - https://www.scopus.com/pages/publications/85124323888

U2 - 10.1109/SMC52423.2021.9658797

DO - 10.1109/SMC52423.2021.9658797

M3 - 会议稿件

AN - SCOPUS:85124323888

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

SP - 921

EP - 926

BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021

Y2 - 17 October 2021 through 20 October 2021

ER -

Zhang M, Fu S, Yan J, Zhang H, Ling C, Shen C et al. An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks. In 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 921-926. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC52423.2021.9658797

An XGBoost-Based Vulnerability Analysis of Smart Grid Cascading Failures under Topology Attacks

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this