# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.1.2 Deep Learning Based PLA
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study Number** & **Approaches** & **Algorithms** & **Evaluation Technique** & **Features** & **Performance Metrics** & **Tools/Platforms** \\ \hline S12 & DL & RNN & Simulated & Channel State & Matrix of detection accuracy & Not mentioned \\ S13 & ML & SVM & Simulated & Time-Frequency & Recognition & MatLab–GNURadio \\  & & & & & Time Fourier Transform) & Rate & GNURadio \\ S14 & ML & KNN & Real test & Frequency & Confusion & \\ S15 & DL & CNN-DNN-RNN & Real test & (using Fast Fourier transform) & Matrix of classification accuracy & MatLab \\ S16 & DL & CNN & Real test & & ROC Curve and confusion & \\ S17 & DL & CNN & Simulated & & Confusion & \\ S18 & ML & SVM & Simulated/Real test & & Matrix of classification accuracy & GNURadio \\ S19 & ML & SVM-DT-KNN & & & & \\ S20 & ML & SVM-DT & Real test & & & \\ S21 & ML & Reinforcement & Real test & & & \\ S22 & DL & DNN-CNN-CPNN & & & & \\  & & & & Response & \\ S23 & ML & SVM-NCA & Simulated & & & \\  & & & & Response & \\  & & & & Response & \\  & & & & Response & \\  & & & & Response & \\ S24 & ML & KNN & Simulated & & & \\  & & & & Responses & \\ S25 & ML & SVM & Simulated & & & \\  & & & & responses & \\ S26 & DL & CNN & Simulated & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 6: _Cont._Figure 8: The performance results of selected studies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study Number** & **Approaches** & **Algorithms** & **Evaluation Technique** & **Features** & **Performance Metrics** & **Tools/Platforms** \\ \hline S28 & DL & GAN & Simulated & Channel State & \begin{tabular}{c} Confusion \\ Matrix of \\ classification \\ \end{tabular} & \begin{tabular}{c} Python (Keras/ \\ TensorFlow) \\ \end{tabular} \\ S29 & DL & \begin{tabular}{c} DNN-CNN- \\ CPNN \\ \end{tabular} & \begin{tabular}{c} Simulated/ Real \\ test \\ \end{tabular} & \begin{tabular}{c} Channel State \\ Information \\ \end{tabular} & \begin{tabular}{c} Authentication \\ Accuracy \\ ROC Curve of \\ classification \\ \end{tabular} & \begin{tabular}{c} USRP \\ Python \\ accuracy \\ \end{tabular} \\ S30 & ML & SVM & Simulated & \begin{tabular}{c} Channel State \\ Information \\ \end{tabular} & \begin{tabular}{c} ROC Curve of \\ classification \\ \end{tabular} & \begin{tabular}{c} USRP \\ Python \\ accuracy \\ \end{tabular} \\ S31 & DL & ANN & Simulated & \begin{tabular}{c} Un phase and \\ Quadrature \\ (Frequency- \\ Amplitude) \\ \end{tabular} & \
\begin{tabular}{c} Classification \\ Accuracy \\ \end{tabular} &
\begin{tabular}{c} MatLab- \\ GNURadio \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 7: Distribution by type. Using deep neural networks (DNN) for multiuser authentication has exceptional performance due to their excellent fitting and classification capabilities, but unfortunately, cannot perform well when the datasets are small. To this end **(S22)** proposed a multiuser authentication scheme that can identify multiple terminals simultaneously with low consumption by combining DNN with data augmentation methods. In **(S15)**, the authors considered deep-learning-based identification of near-field communication (NFC) tags by using RF fingerprinting to enhance security to prevent cloning attack. Various DNN models were adopted to extract RF features and achieve high identification accuracy. They considered three DNN models for RF fingerprinting of NFC tags a fully connected layer-based neural network (FNN), CNN, and RNN. In addition, they considered three popular machine-learning-based algorithms--logistic regression, random forest, and SVM. As a result, the performance of deep-learning-based algorithms surpassed that of conventional machine-learning-based algorithms.