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