An Open-Set Supervised Anomaly Detection Method for Unauthorized Broadcasting Identification

In wireless communications, unauthorized broadcasting within licensed spectrum bands disrupts legitimate signals and interferes with adjacent frequencies, risking critical systems. Existing methods for detecting unauthorized broadcasting often underutilize known signal data, reducing their effectiveness in dynamic, open-set scenarios. To address this, we propose a novel framework combining a temporal convolutional autoencoder (TCAE) with boundary-guided support vector data description (BGSVDD) for accurate detection of unauthorized signals in the radio frequency spectrum. The TCAE captures temporal signal features effectively with an adaptive temporal convolutional network (ATCN), while the BGSVDD uses a small set of known unauthorized samples to create robust decision boundaries with our proposed adaptive misclassification penalty (AMP) loss. Moreover, a global-local support vector (GLSV) strategy enables efficient online model updates, maintaining detection performance in evolving wireless environments with minimal resource overhead. Experiments with real-world broadcast signals show our method outperforms state-of-the-art techniques, especially under challenging interference conditions. Tests on public datasets further confirm its strong generalization across diverse spectrum protection applications.