Primary User Emulation Detection in Wireless Networks with Machine Learning Approach
Honglei Yao, Guangjie Zhu, Yijie Yang · 2021
A novel method based on machine learning approach to detect primary user emulation in cognitive radio networks is proposed. The states of wireless channels are collected. And using the locally weighted linear regression algorithm (LWLR), the number of the available channels in next cycle is predicted in advance. In the paper, the error distribution is estimated and calculated. With a given error, the primary user emulation can be detected in the system. Simulation results demonstrate the prediction results performance with the different thresholds of the prediction error.