  The proliferation of the Internet of Things (IoTs) and pervasive use of many
different types of mobile computing devices make wireless communication
spectrum a precious resource. In order to accommodate the still fast increasing
number of devices requesting wireless connection, more efficient, fine-grained
spectrum allocation and sharing schemes are badly in need. Cognitive radio
networks (CRNs) have been widely recognized as one promising solution, in which
the secondary users (SUs) are allowed to share channels with licensed primary
users (PUs) as long as bringing no interference to the normal operations of the
PUs. However, malicious attackers or selfish SUs may mimic the behavior of PUs
to occupy the channels illegally. It is nontrivial to accurately, timely detect
such kind of primary user emulation (PUE) attacks. In this paper, an efficient
PUE attacked detection method is introduced leveraging the recurrent neural
network (RNN). After a fundamental algorithm using basic RNN, an advanced
version taking advantage of long-short-term-memory (LSTM) is proposed, which is
more efficient on processing time series with long term memory. The
experimental study has provided deeper insights about the different
performances the RNNs achieved and validated the effectiveness of the proposed
detectors.
