Document Type

Article

Publication Date

2026

DOI

10.3390/electronics15132967

Publication Title

Electronics

Volume

15

Issue

13

Pages

2967

Abstract

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary AIDS benchmark. Cross-domain testing reveals that defensive efficacy is strongly constrained by dataset characteristics: small-scale datasets such as MUTAG suffer from persistent trigger concentration, while complex graph manifolds such as PROTEINS exhibit high levels of topological noise. Furthermore, mapping these technical outcomes into an enterprise asset framework yields a 61% expenditure compression at critical technological feeder locations and a 98.93% reduction in total systemic loss. This study indicates that the proposed triangular mitigation strategy offers a valuable, scalable blueprint for enhancing the technical resilience and prognostic economic modeling of critical infrastructure networks.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The complete engineering pipeline is publicly available via our open-access repository at https://github.com/Sabouha-17/ECSMT-Explainable-GNN-Backdoor-Defense (accessed on 20 June 2026)."

Original Publication Citation

Ettahri, S., Enguita, S. P., Chen, C.-H., & Yang, W.-C. (2026). An Explainable CS-Mitigation Triangular (ECSMT) Framework to secure graph neural networks. Electronics, 15(13), Article 2967. https://doi.org/10.3390/electronics15132967

ORCID

0009-0003-7769-0724 (Ettahri), 0009-0009-5048-3964 (Enguita), 0000-0002-4860-9187 (Chen)

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