Single-Edge Node Injection Threats to GNN-Based Security Monitoring in Industrial Graph Systems

• URL: http://arxiv.org/abs/2602.01113v1
• Date: Sun, 01 Feb 2026 09:19:54 GMT
• Title: Single-Edge Node Injection Threats to GNN-Based Security Monitoring in Industrial Graph Systems
• Authors: Wenjie Liang, Ranhui Yan, Jia Cai, You-Gan Wang,
• Abstract summary: Graph neural networks (GNNs) are increasingly adopted in industrial graph-based monitoring systems.<n> adversary that compromises a small number of edge devices may inject counterfeit nodes to bias downstream decisions.<n>This paper formulates deployment-oriented node-injection attacks under constrained resources and proposes the emphSingle-Edge Graph Injection Attack (SEGIA)
• Score: 2.405577583760006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph neural networks (GNNs) are increasingly adopted in industrial graph-based monitoring systems (e.g., Industrial internet of things (IIoT) device graphs, power-grid topology models, and manufacturing communication networks) to support anomaly detection, state estimation, and asset classification. In such settings, an adversary that compromises a small number of edge devices may inject counterfeit nodes (e.g., rogue sensors, virtualized endpoints, or spoofed substations) to bias downstream decisions while evading topology- and homophily-based sanitization. This paper formulates deployment-oriented node-injection attacks under constrained resources and proposes the \emph{Single-Edge Graph Injection Attack} (SEGIA), in which each injected node attaches to the operational graph through a single edge. SEGIA integrates a pruned SGC surrogate, multi-hop neighborhood sampling, and reverse graph convolution-based feature synthesis with a similarity-regularized objective to preserve local homophily and survive edge pruning. Theoretical analysis and extensive evaluations across datasets and defenses show at least $25\%$ higher attack success than representative baselines under substantially smaller edge budgets. These results indicate a system-level risk in industrial GNN deployments and motivate lightweight admission validation and neighborhood-consistency monitoring.

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