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A layer-aware graph attention and patch transformer framework for multi-class IIoT attack classification
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DOI：10.1038/s41598-026-66892-5.png)
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Abstract
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The proliferation of IIoT devices has dramatically expanded the attack surface of critical infrastructure. IIoT systems span multiple communicating tiers from physical sensors to cloud back-ends, attackers increasingly exploit inter-tier boundaries in ways that single-layer detection cannot address. Existing intrusion detection approaches treat network traffic features as unordered, independent values, discarding the structural inter-feature relationships that are characteristic of attack behaviour in layered IIoT architectures. We propose LA-STGAT-PT (Layer-Aware Spatio-Temporal Graph Attention Network with Patch Transformer), an end-to-end deep learning framework for multi-class intrusion detection in IIoT environments. It integrates four components: (1) a Layer-Aware Graph that encodes statistically correlated and architecturally co-located features as a relational graph, enabling tier-aware reasoning over the IIoT protocol stack; (2) a Spatio-Temporal Graph Attention block (STGAT) that simultaneously models feature relationships across the graph and sequential patterns along the feature vector, then adaptively fuses both views; (3) a Patch Transformer Encoder that segments the fused embedding into sub-sequences and applies self-attention to capture long-range inter-group dependencies; and (4) a Dual-Head Output that jointly optimises for attack classification and normal-traffic reconstruction, using elevated reconstruction error as a secondary anomaly signal. Evaluated on the Edge-IIoTset benchmark across 14 attack classes and one normal class, LA-STGAT-PT achieves an overall accuracy of 99.96% and a macro-F1 score of 0.9955. Encoding IIoT protocol-layer topology as a relational graph and combining it with spatio-temporal feature modelling and patch-based self-attention yields state-of-the-art intrusion detection, demonstrating that structural domain knowledge can significantly improve deep learning-based NIDS.
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