PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because users (PU) and secondary (SU) are unable to communicate if a malicious (MU) present. In literature, some techniques used detect attack. However, those do not explore cooperative detection PUE using deep neural (DNN) MCRN network with experimental results on software-defined (SDR). this paper, we design implement an MCRN, including countermeasure based entropy signals, DNN, spectrum sensing (CSS) attacks. A blacklist included fusion center (FC) record data MU. scenarios simulated implemented SDR testbed. Results show that solution increases probability (PD) by 20% for lower signal noise ratio (SNR) values, allowing recording future reference attacker, sharing all SU.