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Detecting anomalous behavior in wireless spectrum is a demanding task due tothe sheer complexity of the electromagnetic spectrum use. Wireless spectrumanomalies can take a wide range of forms from the presence of an unwantedsignal in a licensed band to the absence of an expected signal, which makesmanual labeling of anomalies difficult and suboptimal. We present, SpectrumAnomaly Detector with Interpretable FEatures (SAIFE), an AdversarialAutoencoder (AAE) based anomaly detector for wireless spectrum anomalydetection using Power Spectral Density (PSD) data which achieves good anomalydetection and localization in an unsupervised setting. In addition, weinvestigate the model's capabilities to learn interpretable features such assignal bandwidth, class and center frequency in a semi-supervised fashion.Along with anomaly detection the model exhibits promising results for lossy PSDdata compression up to 120X and semisupervised signal classification accuracyclose to 100% on three datasets just using 20% labeled samples. Finally themodel is tested on data from one of the distributed Electrosense sensors over along term of 500 hours showing its anomaly detection capabilities.

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features