Malicious Attacks for Maximizing Island Numbers in Cyber–Physical Power System: A Graph-Theoretic Approach

This letter proposes an island-maximising attack mechanism to maximise the number of power islands in cyber-physical power systems (CPPS) by disrupting lines. Specifically, a single-level mixed-integer programming (MIP) model is developed with a graph-theoretic approach, which is able to divide CPPS into power islands, while intra-island line overloads are induced in a high-stealth manner with increasing load shedding and economic loss. Case studies conducted on modified IEEE 14-bus and practical 36-zone Great Britain systems validate the effectiveness of our proposed island-maximising approach.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-10-09
DOI
https://doi.org/10.1109/jiot.2025.3619395
Primary Topic
Network Security and Intrusion Detection
Type
article
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article

Malicious Attacks for Maximizing Island Numbers in Cyber–Physical Power System: A Graph-Theoretic Approach

Xin Zhang, Rui Zhang, Jinning Zhang, Min Du
IEEE Internet of Things Journal
Network Security and Intrusion Detection
article

Malicious Attacks for Maximizing Island Numbers in Cyber–Physical Power System: A Graph-Theoretic Approach

Xin Zhang, Rui Zhang, Jinning Zhang, Min Du
article en

Abstract

This letter proposes an island-maximising attack mechanism to maximise the number of power islands in cyber-physical power systems (CPPS) by disrupting lines. Specifically, a single-level mixed-integer programming (MIP) model is developed with a graph-theoretic approach, which is able to divide CPPS into power islands, while intra-island line overloads are induced in a high-stealth manner with increasing load shedding and economic loss. Case studies conducted on modified IEEE 14-bus and practical 36-zone Great Britain systems validate the effectiveness of our proposed island-maximising approach.

IEEE Internet of Things JournalVol. 12(24)
United States Department of Energy (US), University of Leicester (GB), National University System (US), University of Sheffield (GB)
Openalex Percentile: Top 67%
Network Security and Intrusion Detection
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