# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Physical Layer Security  
The world has become increasingly online and connected via wireless networks recently. Additionally, wireless devices are increasingly employed in a variety of sectors. For example, smart things, mobile communication, unmanned platforms, drone control, autonomous driving, etc. Unlike wired networks, the openness of the wireless network allows nearly all wireless receiving devices within their range to receive signals [33; 34]. This feature gives legal and illegal users the same access to the communication channel. However, protecting the integrity, confidentiality, and availability is challenging in wireless networks [33]. Information security mainly depends on cryptographic techniques to achieve communication security requirements, including authenticity, confidentiality, integrity, and availability [2; 4; 35]. Authenticity verifies communicating entities. Data integrity validates that transmitted data are not changed. Data confidentiality assures that transmitted data did not expose to unauthorized entities. Finally, data availability prevents adversaries from interrupting access to data. Using encryption-based security technologies at application layers has enhanced wireless security. Still, their inherent vulnerabilities are heavy computation and key management, resulting in high complexity and resource consumption [2; 35; 36]. Cryptographic techniques have efficiently protected modern communication and computer networks. However, it is not entirely suited to the future of ubiquitous computing, which will be elaborated on in the following. Traditional cryptographic approaches are computationally secure because the attacker cannot decipher the protection within a specific time. However, it may be compromised due to the progress in quantum computing advances. However, because of advances in quantum computing, it may be compromised. For example, the quantum search algorithms such as Grover's and Shor's algorithms exploited the discrete logarithm problem that current cryptographic mechanisms heavily rely on [37]. Traditional authentication techniques are based on the IP or MAC addresses as the identity, which can be easily tampered with by malware attackers [38; 39]. In addition, cryptographic algorithms rely heavily on computational complexity and secret keys [5; 28]. As a result, these algorithms perform effectively on devices with high processing capabilities, like smartphones. In comparison, many IoT devices are low cost and small, equipped with limited storage memory, and powered with batteries, making it impractical to implement complicated cryptography-based security protocols. [PERSON] first considered the confidentiality of PLS was assumed in 1949 and proposed the first application of information theory to cryptology, also known as [PERSON]'s information-theoretic secrecy [40]. Then, approximately three decades later, one of the most targeted studies the physical layer confidentiality is to maximize the secret information rate received by the legitimate user in the wiretap channel, which is defined as the secrecy capacity by [PERSON] [41]. [PERSON]'s work set the basis and inspired PLS research, with scholars proposing various PLS techniques for different purposes. Wireless network security was previously thought to be a high-layer problem that could be handled with cryptographic approaches [42]. The situation changed in the first decade of the 21 st century when wireless networks started to spread around [37]. Therefore, physical layer security based on information theory has appeared as a promising approach to protecting wireless communications to achieve information-theoretic security against eavesdropping attacks, for instance. Compared to cryptographic techniques executed at upper layers, physical layer security offers two significant advantages:  
* First, physical layer security techniques do not rely on computational complexity compared to cryptography techniques [3; 4; 26; 38; 43].