An efficient logic-guided attack strategy on logical-physical coupled networks

Mengying Geng, Ling Zhang, Min Hui, Yiming Huang, Feng Qing, Mengqiao Xu, Changjun Fan and Guangquan Cheng

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: In an era of increasingly interconnected and complex systems, conventional network attack strategies often prove inadequate when confronting the structural and functional intricacies of multi-layer infrastructures. Logical components often lack tangible substances and thus cannot be targeted directly by physical means. Moreover, failures may cascade across layers due to the intricate interweaving of logical and physical layers, further limiting the effectiveness of conventional attack approaches in multi-layer scenarios. To address these challenges, we propose the Critical Edge-to-Node Strike (CENS) framework, which leverages logical-layer guidance to induce efficient damage at the physical layer. CENS bridges logical–physical dependencies by assessing the hybrid centrality of logical edges and employing a max-flow-based graph-cut algorithm to map logical connection damage onto physical nodes. We further introduce a node ablation mechanism that transforms the physical topology to efficiently identify fundamental logical dependencies. This mechanism simplifies path enumeration, improves stability, and enhances attack precision. Extensive experiments on both IEEE118 system and synthetic networks reveal that CENS and its ablation-enhanced variant outperform centrality-based baselines in terms of logical damage ratio, speed, and robustness, achieving up to 60% logical failure with only 15% physical nodes removal. These results demonstrate the effectiveness and superiority of the strategies in logical–physical coupled network attack scenarios.

Keywords: Complex networks; Centrality measure; Network disruption; Multi-layer coupled networks; Cross-layer dependency analysis (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014092

DOI: 10.1016/j.chaos.2025.117396

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