Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection

2026-03-03 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Expert, extended

Summary

This paper introduces a joint multi-objective optimization framework for smart grid attack detection, combining strategic sensor placement with a novel physics-informed graph transformer network (PIGTN). The framework utilizes a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize sensor locations and the PIGTN's detection performance simultaneously, while adhering to practical constraints. The PIGTN model incorporates AC power flow constraints as a regularization term, enhancing its ability to generalize to unseen attacks and improve interpretability. Evaluated across seven benchmark power systems (14, 30, IEEE-30, 39, 57, 118, and 200 bus systems), the proposed framework demonstrates superior robustness under sensor failures and significant improvements in detection performance. Specifically, it achieves up to 37% higher accuracy and 73% higher detection rate compared to other graph network-based variants, with a mean false alarm rate of 0.3%. Optimized sensor layouts also reduce average state estimation error by 61%–98%.

Key takeaway

For AI Scientists and Research Scientists developing smart grid security solutions, this research indicates that integrating physics-informed models with optimized sensor deployment is crucial. You should consider adopting joint optimization frameworks like NSGA-II to simultaneously address sensor placement and detection model training, as this approach significantly improves attack detection accuracy, robustness to sensor failures, and power system state estimation, outperforming traditional separate optimization methods.

Key insights

Jointly optimizing sensor placement and physics-informed graph transformers significantly enhances smart grid attack detection and robustness.

Principles

• Integrate physical laws into neural networks for improved interpretability and generalization.
• Co-optimize sensor placement and detection models for enhanced performance.
• Hybrid node importance scores guide efficient sensor placement in power networks.

Method

The framework uses NSGA-II to jointly optimize sensor placement and PIGTN training. It incorporates a hybrid node importance score for GA initialization and mutation, and adds AC power flow equations as a regularization term to the PIGTN's loss function.

In practice

• Deploy sensors using multi-objective optimization for attack resilience.
• Incorporate AC power flow constraints into graph neural networks.
• Prioritize critical nodes for sensor placement using hybrid centrality metrics.

Topics

• Smart Grid Security
• Attack Detection
• Physics-Informed Neural Networks
• Graph Transformer Networks
• Sensor Placement Optimization

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.