Space information networks (SINs) have the potential to overcome the coverage and reliability limitations of cellular networks. As a result, SINs have received considerable attention as an enabling technology for sixth-generation (6G) networks to support global connectivity. SINs often employ a large number of low Earth orbit (LEO) satellites which are vulnerable to spoofing attacks. Thus, game theoretic physical layer authentication (PLA) based on Doppler frequency spread (DS) and received power (RP) attributes is proposed to provide effective authentication for these satellites. Hypothesis testing with a threshold is used to distinguish between legitimate and illegitimate (spoofer) satellites. Then, a zero-sum PLA game in which the ground station (GS) chooses the optimal detection threshold (τ*) to maximize its utility and a spoofing satellite (s) chooses the optimal attack probability (k*) to maximize its utility. Numerical results are presented to demonstrate the effectiveness of the proposed approach.

Internet of Things (IoT) devices have become ubiquitous due to the prevalence of the Internet. However, the open nature of wireless networks makes them susceptible to spoofing attacks. Moreover, their heterogeneous characteristics create authentication challenges which are increasing due to the tremendous growth in the number and variety of devices. Physical-layer authentication (PLA) provides a solution by utilizing the unique characteristics of wireless channels to aid upper layer authentication (ULA). In this article, an adaptive PLA scheme is proposed which exploits the antenna diversity inherent in multiple-input multiple-output (MIMO) systems. This scheme employs a one-class classification support vector machine (OCC-SVM) with the magnitude and real and imaginary parts of the received signals as features. Results are presented which show that this scheme provides robust authentication. The authentication performance is evaluated considering two majority voting schemes for IoT applications.

The heterogeneous characteristics of wireless mobile networks within the Internet of things (IoT) create authentication challenges due to the large number of devices with diverse requirements and capabilities. Physical layer authentication (PLA) can provide solutions for this heterogeneous environment using wireless channel attributes. In the paper, an adaptive lightweight physical layer authentication (ALPLA) scheme is proposed using machine learning (ML). Antenna diversity at the receiver is exploited to increase the number of features to achieve a high authentication rate (AR). A one-class classifier support vector machine (OCC-SVM) is used with the magnitude and real and imaginary parts of the received signal at each receive antenna as features. One-class classification is a ML technique for outlier and anomaly detection which uses only legitimate training data. The sounding reference signal (SRS) in the 5G uplink radio frame is employed to obtain the features. The proposed scheme is evaluated in an urban environment under different mobility conditions. Results are presented which show that this scheme provides a high AR with sufficient antenna diversity. Further it is superior to other approaches in the literature.