EM-based algorithm for defeating against primary user emulation attacks in cognitive wireless networks

In cognitive wireless networks, the primary user emulation attack (PUEA) is one of the potential security threats. In this work, we attempt to characterize the primary user emulation attack firstly, that is, a malicious cognitive user estimates the channel parameter and transmission power of the primary user (or BS) by using the maximum likelihood estimation (MLE), and then transmits the false signal by emulating these characters to spoof other honest cognitive users making the wrong decisions. In order to defeat against this advanced PUEA strategy, we propose the EM (Expectation Maximization)-based algorithm to counteract the unknown signal by estimating the channel parameters. In our proposed approach, we utilize the difference of the parameters of primary channel and attack channel to achieve the accurate detection through a simple iterative algorithm. At last, we demonstrate the performance of EM detection compared with the naive detection, and the simulation and numerical results illustrate the effectiveness and accuracy of the proposed detection algorithm.