# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.2.2 Review of Selected PLA Technique-based Channel Information
In **(S29)**, a DL-based physical layer authentication approach was suggested that can differentiate between several industrial wireless sensor nodes at the same time while resisting spoofing. Without sacrificing communication resources, the proposed approach can improve the security of industrial wireless networks. Furthermore, three DL algorithms and the spatial diversity of wireless channels can discriminate the sensor nodes without imposing test thresholds and have more practical application values. Convolution preprocessing was employed to reduce the data dimension and extract the CSIs' feature information, resulting in reduced training time and higher authentication accuracy. The authors also discovered that the cost function value declined as the number of iterations increased. On the other hand, to enhance spoofing detection accuracy and efficiency, **(S12)** proposed an intrusion detection scheme that explores CSI to achieve secure and reliable performance. In addition, they employed LSTM as an intelligent classifier to distinguish legitimate users from adversaries. The proposed scheme's results were accurate when evaluated via simulations with different channel conditions. In **(S18)**, researchers proposed an attack detection method based on channel differences. They implemented an automated labelling and learning technique for physical layer authentication to detect clone and Sybil attacks in edge computing industrial wireless networks. The CSI characteristics of a legitimate node are considered distinct from those of adversary nodes. Accordingly, an adversary node has multiple identities but only one physical device; when this adversary node launches Sybil attacks, the CSI from this node remains the same. They executed two attack scenarios to train the proposed method--clone and Sybil attacks. As a result, the detection accuracy rate in clone attacks reached 75%, whereas Sybil attack detection reached 84%. To prevent spoofing and Sybil attacks and ensure the secure access of legal nodes in the IoT area, **(S26)** proposed a physical layer authentication mechanism based on CNN, which utilises the strong coupling relationship between instantaneous CSI and node position. Therefore, CSI deep features are extracted as node identity to foreign unknown nodes within the pre-set area. Any wireless communication system consists of access points and legitimate users, but there may be active eavesdroppers. To detect eavesdroppers who break into the system during the authentication phase, **(S30)** developed a framework for converting wireless signals into structured datasets that machine learning algorithms can use to detect active eavesdropping attacks at the physical layer. Naturally, the performance of a physical layer authentication system is primarily affected by changing propagation and interference conditions. In **(S16)** the authors proposed an approach using an adaptive neural network (ANN) to detect changes in channel characteristics and determine whether an attack has occurred. To overcome the challenge of the physical layer, imperfect and noisy attribute measurements, the preprocessed data were to be authenticated with a wavelet-based noise filter. Then, they created a data-adaptive matrix for input to the classifier. This matrix consisted of a sequence of RSS vectors to capture the time-varying properties of the channel. Even if the data-adaptive channel matrix has improved the difference between legitimate and illegitimate channels, in a real communication environment, the RSS vectors are not necessarily the most practical features to use to differentiate a channel transmission from a spoofer. On the other hand, **(S21)** presented the physical layer authentication algorithms by exploiting the channel states of the radio packets, which determine the test threshold based on reinforcement learning in dynamic wireless networks without knowing the complete channel parameters. Furthermore, **(S28)** explained how the multiple subchannels concept might be used to achieve physical layer authentication.