## 5 Evaluation of ML and DL Models for Spoofing Detection  
This section evaluates the proposed multi-attribute PLA scheme using various ML and DL models. The evaluation metrics include accuracy, precision, recall, F1-score, training time, and memory usage. The models evaluated include LR, RF, KNN, MLP, and CNN. The dataset was balanced through random under sampling and included three critical physical layer attributes: RSSI, BL, and altitude. Standard classification metrics, including accuracy, precision, recall, and F1-score, were used to evaluate each model. Additionally, training time and memory usage were recorded to assess computational efficiency. The key performance metrics for each model are presented in Table 1, while computational efficiency metrics are summarized in Table 2.  
Based on the results in Table 1, RF emerged as the best-performing model, achieving the highest accuracy and F1-score at 83.74%, slightly surpassing other models. LR, MLP, and CNN exhibited comparable performance, with accuracy values around 83%, while KNN lagged with an accuracy of 80.23% and corresponding lower precision, recall, and F1-scores. These findings underscore the varying capabilities of the models in handling the dataset's complexity. LR performed as expected for a linear model, achieving an accuracy of 83.16% and precision, recall, and F1-scores of 87.27%, 83.16%, and 83.19%, respectively. These results reflect its ability to effectively handle linearly separable data. With the shortest training time of approximately 0.002 s and low memory usage of 0.80 MB (as shown in Table 2), LR demonstrated exceptional computational efficiency, making it suitable for real-time applications. RF slightly outperformed LR, achieving an accuracy, recall, and F1-score of 83.74%. It can handle noisy data and capture non-linear relationships, contributing to its superior performance. However, the additional complexity of growing multiple decision trees increased its training time to 0.084 s. Despite this, RF demonstrated efficient memory usage at 0.50 MB, highlighting its ability to balance performance and computational efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**LR** & 83.16\% & 87.27\% & 83.16\% & 83.19\% \\
**RF** & 83.74\% & 87.13\% & 83.74\% & 83.74\% \\
**KNN** & 80.23\% & 80.46\% & 80.23\% & 80.23\% \\ MLP & 83.04\% & 87.08\% & 80.04\% & 83.04\% \\
**CNN** & 83.29\% & 87.21\% & 83.27\% & 83.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics for ML and DL models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Time (s)** & **Memory Usage (MB)** \\ \hline
**LR** & 0.002 & 0.80 \\
**RF** & 0.084 & 0.50 \\
**KNN** & 0.002 & 0.12 \\
**MLP** & 1.83 & 26.3 \\
**CNN** & 4.15 & 6.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational efficiency metrics for ML and DL models. KNN exhibited the lowest performance among the evaluated models, achieving an accuracy of 80.23% and precision, recall, and F1-scores of approximately 80.46%. The proximity-based classification approach struggled with overlapping data points in the feature space, particularly when noisy or variable attributes like RSSI, BL, and altitude were present. While its metrics were lower than other models, KNN demonstrated fast training (0.002 s) and low memory usage (0.12 MB). However, its reduced accuracy makes it less suitable for this application. MLP achieved an accuracy of 83.04% and precision, recall, and F1-scores of 87.08%, 83.04%, and 83.04%, respectively. Its performance is comparable to LR and RF, benefiting from its ability to model non-linear patterns. However, MLP's resource demands were significantly higher, with a training time of 1.83 s and memory usage of 26.3 MB. These resource requirements stem from the iterative weight optimization and fully connected architecture, which necessitate substantial computational resources for backpropagation and gradient descent.