Hammouda, Mohammed2023-12-142023-12-1420232023-12-13http://hdl.handle.net/1828/15703Internet of things (IoT) devices have become ubiquitous and go far beyond smartphones and similar devices. The IoT allows for numerous applications such as smart homes, intelligent healthcare, and intelligent transportation. However, high deployment costs limit cellular network coverage in remote and rural areas, and the reliability of cellular infrastructure during natural disasters is a concern. Thus, space and ground network integration has been proposed to provide global connectivity and support a wide range of IoT applications. Unfortunately, spoofing attacks are problematic due to network complexity and heterogeneity. Authentication for access control is an efficient way to ensure user legitimacy. However, upper layer authentication (ULA) is challenging due to limited computational power, high complexity, and communication overhead. Thus, physical layer authentication (PLA) has been proposed to aid ULA in solving these problems. PLA exploits the fact that legitimate parties and attackers have distinct physical characteristics which are unique between every pair of connected peers based on their spatial locations. In this dissertation, PLA schemes are presented using wireless attributes. First, an adaptive PLA scheme for IoT applications in urban environments is proposed using machine learning (ML) with antenna diversity to increase the number of features. A one-class classifier support vector machine (OCC-SVM) is employed using the magnitude and real and imaginary parts of the received signal at each receive antenna as features. The sounding reference signal (SRS) in the 5G uplink radio frame is employed for this purpose. Results are presented which show that this scheme provides a high authentication rate (AR) with sufficient antenna diversity. Furthermore, an adaptive PLA scheme is presented for collaboration between distributed IoT devices in multiple-input-multiple-output (MIMO) systems. The performance is evaluated considering two majority voting schemes for practical IoT applications. These schemes may be preferable for IoT devices with limited computing capabilities. An adaptive PLA scheme for low earth orbit (LEO) satellites is proposed that employs ML with Doppler frequency shift (DS) and received power (RP) features. This scheme is evaluated for fixed and mobile satellite services at different altitudes. Results are presented which show that the proposed scheme provides better authentication performance using DS and RP features together compared to using them separately. Moreover, PLA using a hypothesis test with threshold or ML for satellite authentication is presented. The results show that the AR with DS is higher than with RP at low elevation angles for both schemes, but is higher with RP at high elevation angles. Further, the ML authentication scheme provides a higher AR than the threshold scheme for a small percentage of the training data considered as outliers, but at larger percentages the OR threshold scheme is better. Finally, game-theoretic satellite authentication using physical characteristics for spoofing detection is presented. Results are given to demonstrate the effectiveness of the proposed approach.enAvailable to the World Wide WebPhysical Layer AuthenticationWireless Communication SecurityMachine LearningMIMO SystemsSatellite CommunicationThesis