  Spectrum anomaly detection is of great importance in wireless communication
to secure safety and improve spectrum efficiency. However, spectrum anomaly
detection faces many difficulties, especially in unauthorized frequency bands.
For example, the composition of unauthorized frequency bands is very complex
and the abnormal usage patterns are unknown in prior. In this paper, a noise
attention method is proposed for unsupervised spectrum anomaly detection in
unauthorized bands. First of all, we theoretically prove that the anomalies in
unauthorized bands will raise the noise floor of spectrogram after VAE
reconstruction. Then, we introduce a novel anomaly metric named as noise
attention score to more effectively capture spectrum anomaly. The effectiveness
of the proposed method is experimentally verified in 2.4 GHz ISM band.
Leveraging the noise attention score, the AUC metric of anomaly detection is
increased by 0.193. The proposed method is beneficial to reliably detecting
abnormal spectrum while keeping low false alarm rate.
