Spectrum Usage Anomaly Detection from Sub-Sampled Data Stream via Deep Neural Network

Abstract

Anomaly detection is an essential part of any practical system in order to remedy any malfunction and accident early to create a secure and robust system. Malicious users and malfunctioning cognitive radio (CR) devices may cause severe interference to legitimate users. However, there are no effective methods to detect spontaneous and irregular anomaly behaviors in sub-sampling data stream from wideband compressive spectrum sensing as an important function of a CR device. In this article, to detect anomaly utilization of spectrum from sub-sampled data stream, a multiple layer perceptron/feed-forward neural network (FFNN) based solution is proposed. The proposed solution would learn the pattern of legitimate and anomalous usages autonomously without expert's knowledge. The proposed neural network (NN) framework has also shown benefits such as more than 80% faster detection speed and lower detection error rate.