Compressed-Sensing-Based Pilot Contamination Attack Detection for NOMA-IoT Communications

Nonorthogonal multiple access (NOMA) technology can significantly promote Internet-of-Things (IoT) networks on spectral efficiency and massive connectivity. However, NOMA-IoT communications are vulnerable to pilot contamination attacks, where the attacker can send the same pilot signals as legitimate IoT users. Most existing countermeasures to this physical-layer threat struggle to adapt to NOMA-IoT networks, in which superimposed signals appear and low-cost IoT devices exist. In this article, we propose a compressed-sensing-based detection scheme to defend against pilot contamination attacks in NOMA-IoT networks. In particular, we present a multiple measurement vector (MMV) compressed sensing model and a security spreading code generation (SSCG) framework to prevent pilot contamination attacks from spoofing base station (BS) in NOMA-IoT networks. Furthermore, to efficiently reconstruct the superimposed signals based on the SSCG framework, a matching pursuit (MP) multiple response sparse Bayesian learning (MSBL) algorithm (MP-MSBL) is proposed. The security analysis and algorithm complexity of the proposed algorithms are provided. The simulation results evaluate and confirm the effectiveness of the proposed detection schemes. The reconstruction and detection accuracy of pilots can be higher than 99% under different scenarios.