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

TL;DR

This paper introduces Graph WPT+HOS, a novel, label-free anomaly detection method for IIoT networks that fuses spatial, time-frequency, and non-Gaussian features, achieving superior performance under challenging wireless conditions.

Contribution

The paper presents a new fusion-based anomaly detector combining GFT, WPT, and HOS, optimized for edge hardware and robust against fading and domain shifts.

Findings

Achieves highest ROC-AUC and PR-AUC across tests

Reduces detection latency significantly

Operates efficiently on ARM hardware without GPU

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.