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arXiv · 2605.03591

Graph-Spectral Fusion of Wavelet Packets and Higher-Order Statistics for Anomaly Detection in Industrial IoT Networks

Surya Jayakumar·Indrakshi Dey
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Abstract

Industrial Internet of Things (IIoT) networks demand reliable anomaly detection under harsh wireless conditions, yet most detectors fail on four fronts: hostile fading, stealthy non-Gaussian faults, discarded spatial structure, or constrained edge hardware. We propose Graph WPT+HOS, a classical label-free detector that fuses three complementary views: the Graph Fourier Transform (GFT) for spatial inconsistency, the Wavelet Packet Transform (WPT) for transient time-frequency localization, and Higher-Order Statistics (HOS) for non-Gaussian shape. The fused features are scored by a Mahalanobis distance with Ledoit-Wolf shrinkage and converted to alarms by a one-sided CUSUM. The pipeline is asymptotically optimal at the decision stage, requires no labeled anomalies, and runs on ARM-class edge hardware without GPU acceleration. Across six baselines and four domain-shift regimes under Rayleigh fading, Graph WPT+HOS attains the highest ROC-AUC and PR-AUC and reduces CUSUM detection latency.

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Surya Jayakumar, Indrakshi Dey. 2026-05-05. Graph-Spectral Fusion of Wavelet Packets and Higher-Order Statistics for Anomaly Detection in Industrial IoT Networks. https://arxiv.org/abs/2605.03591

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