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
N1 - Publisher Copyright:
© 2021 IEEE.
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 -
