# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.1.2 Deep Learning Based PLA  
In recent years, the deep learning computing approach has been termed the Gold Standard in the machine learning community [99]. It has gradually become the most extensively used computational approach in machine learning, generating exceptional results for various complex cognitive tasks. More importantly, DL has outperformed well-known ML techniques in many domains because of its powerful data analysis ability and has proved to be robust, dependable, and accurate. As a result, DL has recently been increasingly combined with physical layer authentication and secure wireless communication systems. Various studies selected convolutional neural networks (CNN) for their reliability and powerful learning features with high accuracy and low loss function in the training process. In **(S7)**, three classification approaches were applied for the physical layer authentication problem, based on a combination of CNN and recurrence plots (RP) techniques. The recurrence plots technique is considered a visualization tool that aims to explore a multidimensional phase space trajectory through a 2D representation of its recurrences. The RP technique was used to visualize the recurrences of dynamic systems in the context of PLA and transform the digitized RF emissions before submitting them to a CNN classifier. To secure wireless communication in the WLAN system, **(S10)** proposed a methodology based on CNN to identify different devices by taking advantage of unique RF fingerprints. For multi-user authentication, **(S17)** proposed a CNN model to distinguish multiple legitimate transmitters and one spoofer in the MIMO-OFDM system, in which the proposed model can authenticate legitimate users and detect attackers by CSIs with higher performance compared to traditional hypothesis test-based methods. To prevent spoofing and Sybil attacks, and ensure that authorized nodes in the IoT area have secure access, **(S26)** proposed a physical layer authentication mechanism based on CNN, using the association between instantaneous CSI and node position. Further, **(S16)** presented an adaptive neural network method to learn the features of the legitimate channel and perform spoofer detection. In addition, the study implemented the data-adaptive matrix and CNN frameworks for feature representation and classification. The results showed that the artificial neural network (ANN)-based method can significantly detect spoofing attackers, although the results are sensitive to the actual experimental scenario. Therefore, the adaptive neural network-based authenticator offers quick access authentication and improved security performance in wireless networks. On the other hand, researchers in **(S29)** adopted three DL algorithms to implement physical layer authentication in industrial wireless sensor networks to meet low-latency requirements DNN, CNN, and the convolution preprocessing neural network (CPNN). Furthermore, they focused on the impact of different hidden layer numbers on authentication rates using the proposed method; the more hidden layers there are, the faster the neural network's performance converges. Therefore, the authentication success rate increased as the number of hidden layers increased. Regardless, the method's performance did not con tinue to improve and tended to stabilize once the number of hidden layers reached a certain number, due to the inherent characteristics of the specific wireless channels. The CNN and CPNN algorithms had good authentication performance and an ultra-short retraining time, whereas DNN had the best authenticating performance. \begin{table}
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