The widespread application of wireless communication has led to increasingly complex electromagnetic environments, where spectrum abuse and malicious interference frequently cause abnormal signals. Radio anomaly detection technology has emerged to address these challenges. This paper focuses on the performance limitations of existing radio anomaly detection methods under low interference-to-signal ratio (ISR)conditions. We propose a fusion detection algorithm, LOF-OCSVM, integrating local outlier factor (LOF) and one-class support vector machine (OCSVM). Innovatively, we introduce three novel features: fluctuation entropy (FE), fluctuation mutual information (FR-MI), and lognormal distribution fitting parameters derived from signal fluctuation sequences. These features quantify the disorderliness, adjacent correlation, and statistical distribution characteristics of signal fluctuations, significantly enhancing the detection sensitivity for weak interference signals. Simulation experiments demonstrate that: feature effectiveness: the new features improve recall by >30% and F1-score by >23% at −20 dB ISR. Model superiority: the LOF-OCSVM fusion model achieves an F1-score of 0.8634 at −20 dB ISR through a hierarchical decision mechanism, outperforming single model and simple hybrid approaches. Robustness: compared to Deep SVDD and E-GAN, our method improves AUC by 3% under low ISR conditions while maintaining stronger robustness.

Emerging intelligent systems rely on secure and efficient signal processing to ensure reliable operation in environments where there is limited prior knowledge and significant interference. Single-channel blind source separation (SCBSS) is critical for applications such as wireless communication and sensor networks, where signals are often mixed and corrupted. Variational mode decomposition (VMD) has proven effective for SCBSS, but its performance depends heavily on selecting the optimal modal component count k and quadratic penalty parameter $\alpha$. To address this challenge, we propose a secure and intelligent SCBSS algorithm leveraging adaptive VMD optimized with Improved Particle Swarm Optimization (IPSO). The IPSO dynamically determines the optimal k and $\alpha$ parameters, enabling VMD to filter noise and create a virtual multi-channel signal. This signal is then processed using improved Fast Independent Component Analysis (IFastICA) for high-fidelity source isolation. Experiments on the RML2016.10a dataset demonstrate a 15.7% improvement in separation efficiency over conventional methods, with robust performance for BPSK and QPSK signals, achieving correlation coefficients above 0.9 and signal-to-noise ratio (SNR) improvements of up to 24.66 dB.