# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.1.2 Deep Learning Based PLA
The study **(S31)** indicated that the inherent RF properties originating from the manufacturing process in a wireless device can be exploited as strong physical, unclonable functions for device authentication in asymmetric IoT networks without any additional hardware at the transmitters. Furthermore, a lightweight machine learning framework was designed to compensate for receiver non-idealities and simultaneously account for both data and channel variability. Since this is a nonlinear multidimensional classification problem, an ANN was employed as a learning engine. On the other hand, **(S12)** utilised recurrent neural network's (RNN) architecture in their study, in which they designed an intrusion detection technique that explores CSI to achieve secure and reliable performance in detecting adversarial intruders intelligently and precisely. Furthermore, to enhance detection accuracy and efficiency, they employed long short-term memory (LSTM) as an intelligent classifier to distinguish legitimate users from malicious intruders. The performance of the proposed LSTM-based detection technique was evaluated via simulations with variable channel conditions. To evaluate the severity of adversarial attacks in wireless communications, **(S3)** considered an unauthorized transmitter trying to produce fake signals classified as authorized using deep learning. The researchers achieved this by recasting the problem as a reinforcement learning problem and proposed using policy gradient approaches to accomplish transmitter spoofing in a wireless network. They created a model that adds developed perturbations to the I/Q samples delivered by an adversarial transmitter to deceive the authenticator into identifying it as a legitimate transmitter. The proposed method considered neural network architecture for generators that employed LSTM networks; for discriminators, they used residual networks. The results demonstrated that it is possible to deceive a deep learning-based authenticator with high success rates even at low SNR. However, because deep learning-based transmitter classification algorithms require training data to train the classifier, they are limited to known transmitters. As the attacker is unknown to the receiver, the receiver cannot acquire the attacker's data to train these machine learning classifiers. Thus, these techniques are ineffective in the presence of an unknown attacker. A generative adversarial network (GAN)-based robust wireless transmitter identification scheme is proposed in **(S1)**; the authors offered a GAN-based wireless transmitter identification scheme to detect malicious attackers and classify trusted transmitters. Adversarial entities, such as rogue transmitters, might manipulate the signal and data when employing ML techniques for communication networks using different targeted data-generation techniques. In **(S2)**, the researchers demonstrated the use of GAN for the task of robust transmitter identification. The proposed GAN architecture has two primary components: a generative model that generates false data using given data distribution, and a discriminative model that estimates the probability that a sample came from the training data rather than the generative model. In the same context, **(S28)** developed an authentication model based on the physical layer in an adversarial environment. They employed the GAN model to detect the adversary, in which the discriminative model received either authentic samples from the training data or fake samples generated by the generative model. The generative model produced fake samples based on a function from random variable input and parameters. The discriminative model allocated a probability from zero to one based on whether the sample was fake or authentic. The discriminative model was trained to maximize the probability of allocating the correct label, whereas the generative model minimizes the same probability. RQ2:What Are the Existing Physical Layer Authentication Techniques for Handling a Wireless Network's Security Problems?