@article{Wang2026, 
author = {Kelan Wang and Jianfei Chen},
title = {Hierarchical Cyber–Physical Symbiosis with Bidirectional State Space Modeling for IIoT Anomaly Diagnosis},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {1},
keywords = {Industrial Internet of Things, cyber–physical anomaly diagnosis, state space model, multi-scale temporal modeling, network traffic analysis},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.079644},
doi = {10.32604/cmc.2026.079644},
abstract = {As 6G-enabled Industrial Internet of Things (IIoT) evolves, green and sustainable industrial monitoring increasingly relies on edge AI to deliver low-latency diagnosis under tight resource constraints. Industrial cyber–physical systems increasingly rely on heterogeneous sensing and communication infrastructures, where network-side attacks can propagate into physical processes and appear as coupled anomalies. Reliable diagnosis therefore requires joint learning from time-synchronized cyber and physical telemetry rather than modeling them as independent signals. This paper develops Cyber–Physical Symbiosis Network (CPSNet), a model designed for edge-AI deployment with a dual-stream architecture for fixed-window multiclass cross-domain anomaly diagnosis in IIoT. CPSNet encodes each modality into hierarchical multi-resolution features and refines them with a Multi-Scale Bidirectional-Recursive (MSBR) block. MSBR couples multi-kernel temporal convolutions with a gated bidirectional state space pathway, capturing transient irregularities while retaining long-range context within the window. Cross-modal dependency is injected at every scale by a symbiosis module that performs bidirectional channel-wise gating and holistic state space fusion to learn unified cross-modal dynamics efficiently. A compact multi-scale pooling head with auxiliary modality supervision preserves discriminative evidence in both streams. On the DataSense benchmark, CPSNet achieves 97.18% Accuracy and 99.04% AUC on Multiclass-8, and 89.07% Accuracy and 94.28% AUC on Multiclass-50, showing consistent improvements over single-modality and multi-modal baselines. Ablation and efficiency analyses further suggest complementary gains from multi-scale refinement and explicit coupling with a favorable accuracy–runtime trade-off. These results suggest that hierarchical cross-modal coupling with state space temporal modeling can improve robust, fine-grained IIoT diagnosis for 6G edge-AI monitoring.}
}