# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 1 Introduction  
With the development of wireless communications and networks, wireless networks have been widely used in various sectors, such as health care, industry, education, and the military, and have become closely related to people's daily lives. Statista Inc. expects 30.9 billion Internet of Things (IoT) devices to be connected to the internet worldwide by 2025; examples of IoT connections include connected cars, smart home devices, and industrial equipment. In comparison, non-IoT connections--smartphones, laptops, and computers with connections--are predicted to surpass 10 billion units [1]. Furthermore, the various wireless devices' increased data rates, superior network capacity, and openness in communication carriers make wireless communication and networks more vulnerable to attacks. As a result, security has become a critical issue for the future of wireless networks [2; 3]. A secure wireless communication system includes authentication and confidentiality [4; 5]. Authentication verifies the user's identity and prevents adversarial users from accessing the network, while confidentiality guarantees that eavesdroppers are unable to read confidential messages using encryption schemes. Traditional security systems use symmetric and asymmetric cryptographic algorithms to ensure communication confidentiality and authentication. Upper-layer cryptography techniques have been adopted to provide wireless security. Cryptography is a conventional technique to secure a system by utilising the upper layers of the open systems interconnection (OSI) model [3]. Due to the rapid development of ubiquitous computing, traditional security mechanisms, such as cryptographic techniques, have not been efficient in protection. However,traditional security mechanisms face a variety of challenges [6]. For example, the constrained resources of ubiquitous computing devices may not support the computational needs of cryptography authentication techniques that require sufficient resources and high computing ability [7]. Furthermore, with developments in quantum computing, adversaries may be able to execute analytical or brute force attacks, which can be disastrous for any cryptosystem [5]. Likewise, adversaries can easily tamper with traditional authentication mechanisms based on media access control (MAC) or internet protocol (IP) addresses. Consequently, the transformative revolution that ubiquitous computing aims to bring about could be compromised by a lack of secure connectivity. Furthermore, the openness of wireless communication in ubiquitous computing leaves the network unprotected from malicious attacks that imitate legitimate user identities [8]. Therefore, a quick and efficient lightweight authentication method is required to identify unknown wireless transmitters and resolve security threats introduced by adversaries. Recently, many limited-capacity devices and users have demanded latency-sensitive services that require lightweight authentication techniques [9]. Meanwhile, physical layer security techniques have appeared that provide lightweight security solutions. Physical layer security (PLS) is based on unique physical layer characteristics, including radiofrequency fingerprint (RFF), wireless channel state information (CSI), receive signal strength (RSS), and channel impulse response (CIR), to provide security services. As a result, the physical layer characteristics of wireless channels and hardware have become unique and consistent, which are hard to alter by users and can be used to identify wireless transmitters. Machine learning (ML) is a subset of artificial intelligence (AI) that emerged from pattern recognition [10]. Lately, research in wireless communication has noted the distinction and effectiveness of machine learning by identifying the probability of learning based on signal classification [11] and specific emitter identification [12; 13]. However, ML algorithms may face difficulty handling high-dimensional data because of the sizeable signals of raw data.