# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.2.1 Review of Selected PLA Technique-based Radio Frequency Fingerprinting
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Study Number** & **Authentication Techniques** & **Proposed Solutions** \\ \hline S20 & CSI & Physical layer authentication method-based machine learning and channel features for the 5G wireless communication security by determining whether the received packets are from a legitimate transmitter or a counterfeiter. \\ S21 & RSSI & PLA spoofing detection schemes based on ML in wireless networks. \\ S22 & CIR & Deep learning based physical layer authentication framework to enhance the security of industrial wireless sensor networks. \\ S23 & CIR & Machine learning based physical layer authentication scheme in the multi-input and multi-output wireless communication environment. \\ S24 & CFR & Physical layer authentication approaches based on statistical and machine learning techniques. \\ S25 & CSI & Wireless physical layer channel authentication classifier combined with machine learning algorithm. \\ S26 & CSI & Physical layer authentication mechanism based on deep learning and wireless channel fingerprints to distinguish sending nodes in different locations. \\ S27 & CFR & Physical layer authentication scheme for multiuser to improve the accuracy of authentication in dynamic industrial scenarios. \\ S28 & CSI & Physical layer authentication method uses an adversarial neural network and measured multiple-input multiple-output communications channel information to decide on whether to authenticate a particular device. \\ S29 & CSI & Deep learning based physical layer authentication methods to enhance the security of industrial sensor networks by utilizing the spatial diversity of wireless channels. \\ S30 & CSI & Machine learning classifiers are considered to detect the eavesdropper who breaks into the system during the authentication phase. \\ S31 & RFF & Radio frequency Physical unclonable function method where allows real-time authentication of wireless nodes that not require any additional circuitry for generation or feature extraction. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._techniques, on the other hand, require a compromise between the rate at which authorized radios' IDs are verified vs. the rate at which rogue radios are rejected. As a result, increasing the ID verification rate necessitates sacrificing the rogue rejection rate. This study successfully demonstrated the performance result of true ID verification \(\geq\) 90% at SNR \(\geq\) 6 dB and the false rejection of rogue radio \(\leq\)10% at SNR \(\geq\) 3. However, the dependency between verification and rejection rates leads to degraded model performance at lower SNR values. Other studies used the physical characteristics of wireless devices to create unique authentication. In **(S7)** CNN combined with recurrence plots techniques was applied to represent the RF signal in 2D. The RF signal was sampled directly in the in-phase and Quadrature formats and then synchronized and normalized offline to extract the bursts of traffic associated with each payload. Moreover, **(S10)** even suggested a deep learning-based technology that used RF fingerprinting to recognize different devices accurately for secure wireless communication in a WLAN system. For device authentication, **(S14)** considered a method for Wi-SUN device authentication by extracting physical layer features, such as frequency deviation of FSK modulation and clock frequency offset, which differ slightly from device to device due to RF imperfections. Their proposed method successfully classified devices with 100% accuracy by observed physical layer fingerprints. To solve the problem of identifying multiple of the identification of multiple wireless transmitters,. In **(S13)** used, the one-against-one SVM algorithm based on RF fingerprints through a short time Fourier transform to realize the classification and recognition of multiple wireless devices. They authors concluded that the correct recognition rate increased continuously rate increases continuously as the SNR increased. SNR increases.