# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.1.1 Machine Learning Based PLA  
Machine learning is a subset of artificial intelligence that emerged from pattern recognition. The first definition of machine learning was provided six decades ago by [PERSON]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Study Number** & **QA1** & **QA2** & **QA3** & **QA4** & **QA5** & **QA6** & **QA7** & **QA8** & **QA9** & **QA10** & **Total** \\ \hline S1 & 1 & 0.5 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 9.5 \\ S2 & 1 & 1 & 0.5  
Samuel [97]. Machine learning approaches exploit past data for model generation and adapt to their conditions through experience. These developed models can generate outputs from given inputs without human intervention. The ML algorithm can observe the relationship between input and output because of the large amount of data available and repetitive interactions. Some studies presented a classification method based on support vector machine (SVM). The SVM algorithm has great theoretical and practical advantages when handling difficulties like small samples, nonlinearity, and high dimensions. Study **(S6)** proposed a classification method based on SVM to identify cloning RF devices and deal with binary classification problems. However, the study compared classification models one based on traditional logistic regression (LR) and another based on SVM. The authors found that the accuracy of LR is not as good as SVM. In the same context, S11 developed a model using RF-distinct native attribute fingerprints and an SVM algorithm for device verification and rogue device rejection. Furthermore, **(S25)** proposed a channel authentication method based on an SVM algorithm to enhance the performance of the authentication detection method and decrease the hardware's complexity. To aid the identification models in multiple wireless transceiver scenarios, **(S13)** presented a model based on SVM to identify multiple wireless transceivers. In addition, **(S27)** suggested a lightweight multiuser PLA mechanism to prevent illegal nodes from falsifying the identity information of legitimate nodes to access industrial wireless networks. The mechanism is based on SVM, which utilizes down-sampling and parameter optimization to reduce computational complexity and solve the problem of low authentication accuracy. To obtain the best multi-classification, three optimization algorithms were applied a grid search algorithm, a particle swarm optimization algorithm, and a genetic algorithm to search for the best parameter pair. The study **(S8)** proposed an integrated support vector data description (SVDD) model to solve identity authentication problems in the mobile IoT. SVDD is established based on statistical learning theory, inherits its advantages, develops continuously, and has a complete theoretical foundation and basis. The study also used the whale swarm optimization algorithm (WOA) to optimize the parameters in the SVDD model. This algorithm has the advantages of fewer adjustment parameters, simple operation, and strong local optimal ability. Moreover, **(S23)** presented an SVM classifier combined with a neighbourhood components analysis (NCA) algorithm for reliable feature selection. The NCA placed the same label of feature components close to each other, and the cases of different labels were placed as far away as possible in the feature space. To detect adversary attacks, **(S18)** developed two models to detect clone and Sybil attacks using CSI. The authors utilised SVM as an attack detection algorithm for two main reasons. First, attack detection is a two-class problem with only two results. Second, the SVM algorithm probably achieves better classification results with small offline training sample sets. Furthermore, to detect an eavesdropper during the authentication phase, **(S30)** applied sophisticated SVM classifiers, in which they constructed two models, depending on whether the channel state information (CSI) was known or unknown.