Abstract
Cognitive radio networks (CRNs) are highly vulnerable to spectrum spoofing, PU emulation (PUE), and harmonic RF manipulation attacks, which significantly degrade dynamic spectrum access reliability and communication security. This paper proposes a harmonic trust spectrum modeling framework for cognitive radio intrusion detection using multi-domain RF intelligence and adaptive machine learning. The proposed system integrates harmonic spectrum analysis, trust oscillation modeling, spectral entropy analysis, wavelet decomposition, and power spectral density (PSD) characterization to detect anomalous RF behaviors. Three heterogeneous RF datasets were used for experimental evaluation: the RadioML 2016.10A modulation dataset, the Oracle RF fingerprinting IQ dataset, normal signals generated by GNU Radio, and malicious PUE RF signals. The proposed methodology extracts advanced harmonic trust features including spectral entropy, spectral flatness, harmonic trust instability index (HTII), and oscillatory trust divergence (OTD), which are subsequently processed using an adaptive XGBoost-based intrusion detection system. Experimental results demonstrate that malicious RF users exhibit significantly higher spectral instability, irregular harmonic distributions, elevated entropy behavior, and oscillatory trust divergence, compared to legitimate users. Furthermore, studies covering fast Fourier transform (FFT) harmonic spectra, PSD analysis, wavelet coefficient analysis, trust oscillation curves, and confusion matrix evaluations confirm the effectiveness of the proposed framework. The proposed harmonic trust spectrum model provides a computationally efficient and scalable solution for next-generation secure cognitive radio communications and RF cyber defense systems.