  Automated wireless spectrum monitoring across frequency, time and space will
be essential for many future applications. Manual and fine-grained spectrum
analysis is becoming impossible because of the large number of measurement
locations and complexity of the spectrum use landscape. Detecting unexpected
behaviors in the wireless spectrum from the collected data is a crucial part of
this automated monitoring, and the control of detected anomalies is a key
functionality to enable interaction between the automated system and the end
user. In this paper we look into the wireless spectrum anomaly detection
problem for crowdsourced sensors. We first analyze in detail the nature of
these anomalies and design effective algorithms to bring the higher dimensional
input data to a common feature space across sensors. Anomalies can then be
detected as outliers in this feature space. In addition, we investigate the
importance of user feedback in the anomaly detection process to improve the
performance of unsupervised anomaly detection. Furthermore, schemes for
generalizing user feedback across sensors are also developed to close the
anomaly detection loop.
