# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 1 Introduction
Innovations in ML delivered a new learning technique called deep learning (DL) [10]. Deep learning has improved image and speech recognition by solving complicated multiclass classification problems [14]. Deep learning is a machine learning algorithm that models the function of increasing complexity by adding more layers and more nonlinear processing neurons within a layer; it can learn higher-level representations of input data [15]. Therefore, deep learning is a suitable model for this problem because it can learn from data from different wireless signals. With the evolution of deep learning in recent years, researchers have begun to use DL techniques for classifying a wireless signal in terms of signal recognition [16; 17; 18; 19] and modulation scheme classification [20; 21; 22]. In general, one can hardly guarantee the secure communications of many ubiquitous computing devices with limited wireless resources and challenging issues in developing security techniques. Ubiquitous computing is located in the heterogeneous networks nearest the users and adversaries who can easily capture the terminals and launch malicious activities. Secure authentication in ubiquitous computing is also complicated by the promiscuous nature of the wireless transmission medium and the limited hardware and software capabilities of the nodes in such networks. Furthermore, most ubiquitous computing devices have limited resources, computation, and power. Accordingly, it is impossible to perform data caching and follow traditional cryptography-based security computations. As a result, a lightweight and secure authentication technique needs to be proposed. By taking advantage of the physical layer characteristics of transmission media, physical layer authentication (PLA) can execute lightweight authentication as well as successfully manage the trade-off between the security and low-latency requirements of the wireless nodes compared with the upper-layer authentication mechanisms [23]. To the best of our knowledge, no previous study has conducted a systematic literature review (SLR) on physical layer authentication in wireless network-based machine learning approaches, which makes it hard to determine the maturity and proficiency of physical layer authentication techniques to solve wireless networks' security challenges. Accordingly,this paper conducts a systematic review to identify, analyse, and evaluate existing PLA solutions used to secure wireless communication and networks. To this end, we aim to answer the following research questions:  
* What machine learning approaches are applied in physical layer authentication to secure a wireless network? * What are the existing physical layer authentication techniques for handling a wireless network's security problems? * What are the existing key challenges, open issues, and future trends in wireless network security based on physical layer authentication solutions? To conduct this SLR, we followed the methods introduced by [PERSON] [24] to provide a clear vision of the existing research and highlight new research guidelines. This SLR has resulted in constructing and delivering a knowledge base of current PLA techniques to secure the context of wireless networks for practitioners seeking to comprehend the current techniques and methods applied and researchers aiming to explore existing gaps. The main contributions of this paper are as follows:  
* Present a systematic review of the current state of the art in physical layer authentication based on machine learning and deep learning approaches. * Provide an assessment of ML and DL algorithms used for physical layer authentication. * Review the methods of physical layer authentication and compare their performance. * Outline the primary challenges and issues confronting physical layer authentication techniques. * Define the key aspect in which future research can improve the use of PLA for wireless network security. The remainder of the paper comprises five sections.