# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.1.2 Deep Learning Based PLA
The various physical layer authentication techniques for a wireless network's security problem offered by the studies included in this review are presented in this section. In addition, we addressed the use of radio frequency fingerprinting and channel-based techniques, as well as the research papers that have been written on each. Finally, Table 7 summarizes the PLA techniques and proposed solutions in the existing studies. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study Number** & **Authentication Techniques** & **Proposed Solutions** \\ \hline S1 & RFF & Radio frequency fingerprint classifier consists of multiple discriminators to both detect malicious attackers and classify trusted transmitters. \\ S2 & RFF & Radio frequency adversarial learning model generates fake signals and distinguishes trusted transmitters from rogue ones. \\ S3 & RFF & Deep learning-based classifier to evaluate the feasibility of using physical layer authentication by introducing an algorithm that adds learned perturbations transmitted by an adversarial transmitter to fool the authenticator and classifying it as an authorized transmitter. \\ S4 & RFF & Radio frequency fingerprint security mechanism to avoid the man-in-the-middle attack in industrial IoT scenario. \\ S5 & RFF & Method based on radio frequency fingerprint to enhance wireless network security and distinguish different wireless network devices. \\ S6 & RFF & Technology based on radio frequency fingerprint to find the difference among devices and identity them. \\ S7 & RFF & Deep learning-based approach for the authentication of IoT wireless devices with the same model. \\ S8 & RFF & Radio frequency fingerprint authentication model to solve identity authentication problems in the mobile IoT. \\ S9 & RFF & Inject a designed radio frequency fingerprint into the wireless physical layer to increase the security of a stationary IoT device with minimal overhead. \\ S10 & RFF & Radio frequency fingerprint models utilize raw baseband In-phase and Quadrature samples to identify the transmitting radio. \\ S11 & RFF & Physical layer IoT authentication approach based on radio frequency fingerprint to successfully authorize identity (ID) verification and rejection of all rogue radio ID spoofing attacks. \\ S12 & RFF & Implement an intrusion detection scheme to determine whether a spoofing attack happens. \\ S13 & RFF & Radio frequency fingerprint method to identify the same wireless transceiver module. \\ S14 & RFF & Radio frequency fingerprint classifier based on machine learning to identifies the authorized wireless smart utility network devices. \\ S15 & RFF & Near field communication tags identification method based on deep learning and radio frequency fingerprint to enhance the security by preventing the cloning attack. \\ S16 & RSS & Adaptive neural network authentication process to improve from attack detection and leading to effective physical layer security. \\ S17 & CSI & A multi-user authentication system security to detecting spoofing attacks in wireless networks. \\ S18 & CSI & Automated labeling and learning method for physical layer authentication where detect clone and Sybil attacks in edge computing industrial wireless network. \\ S19 & CSI & Physical layer authentication approach based on machine learning algorithms in industrial wireless cyber-physical systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Authentication techniques, proposed solution of existing studies on PLA.