Random-Training-Assisted Pilot Spoofing Detection and Security Enhancement

The pilot spoofing attack is considered as an active eavesdropping activity launched by an adversary during the reverse channel training phase. By transmitting the same pilot signal as the legitimate user, the pilot spoofing attack is able to degrade the quality of legitimate transmission and, more severely, facilitate eavesdropping. In an effort to detect the pilot spoofing attack and minimize its damages, in this paper we propose a novel random-training-assisted (RTA) pilot spoofing detection algorithm. In particular, we develop a new training mechanism by adding a random training phase after the conventional pilot training phase. By examining the difference of the estimated legitimate channels during these two phases, the pilot spoofing attack can be detected accurately. If no spoofing attack is detected, we also present a computationally efficient channel estimation enhancement algorithm to further improve the channel estimation accuracy. For the case of the missed detection of the spoofing attack, we also propose a secure transmission algorithm for downlink data transmission to enhance the security. Extensive simulation results demonstrate that the proposed RTA scheme can achieve efficient pilot spoofing detection in all cases and its performance is superior to other state-of-the-art detectors.