## 5 Experimental Results
### Window Size Manipulation + Attention Mechanism + FGSM
The hybrid model performance based on the CWL2 method is presented in Table 5. The results show that the CWL2 method is unsuitable for this model and dataset. The model's performance metrics are low, and the error metrics are very high. This indicates that the model's predictions are often incorrect, and the responses are often very different from the actual values. These results show that the model cannot learn effectively on this dataset, and the predictions often need to be more accurate. The parameters of the proposed attention mechanism + CWL2 model are as follows: epsilon = 0.001. The epsilon determines the step size during the Carlini-Wagner (CW) attack and optimization in the training function. It is used as the learning rate (Ir) in the CW attack. The hybrid model performance based on the JSMA method is represented in Table 6. The results show that the metrics for different window sizes are low. For example, the sensitivity obtained in the range of (20-40) is 0.079%, the accuracy is 0.001%, and the F1 score is 0.058%, which are low values. This situation shows that the model's classification ability needs to be improved and it needs to perform better on the data. Especially in the range of (40-60), the accuracy and sensitivity values of zero reveal that the model cannot produce any correct predictions in this data range. The error metrics also reach rather high \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Window Size** & **Precision** & **Accuracy** & **Recall** & **F1 Score** & **MAE** & **MAD** & **Maximum Absolute Error** \\ \hline
20–40 & 0.2037 & 0.617 & 0.204 & 0.182 & 0.0070 & 0.0018 & 21.4162 \\ \hline
40–60 & 0.1045 & 0.295 & 0.136 & 0.086 & 0.0124 & 0.0086 & 20.5604 \\ \hline
60–80 & 0.1317 & 0.480 & 0.138 & 0.115 & 0.0096 & 0.0031 & 20.4977 \\ \hline
80–100 & 0.146 & 0.241 & 0.145 & 0.120 & 0.0107 & 0.0079 & 21.7364 \\ \hline
100–120 & 0.113 & 0.322 & 0.137 & 0.089 & 0.0095 & 0.0069 & 20.4732 \\ \hline
120–140 & 0.082 & 0.213 & 0.077 & 0.028 & 0.0082 & 0.0032 & 20.5589 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hybrid model performance based on PGD method. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Window Size** & **Precision** & **Accuracy** & **Recall** & **F1 Score** & **MAE** & **MAD** & **Maximum Absolute Error** \\ \hline
20–40 & 0.00002 & 0.00033 & 0.0625 & 0.00004 & 2256.11 & 1406.3 & 4935.39 \\ \hline
40–60 & 0.1202 & 0.1177 & 0.1106 & 0.0853 & 0.0084 & 0.0059 & 21.8995 \\ \hline
60–80 & 0.00002 & 0.0003 & 0.0625 & 0.00004 & 690.14 & 365.54 & 1567.36 \\ \hline
80–100 & 0.00002 & 0.0003 & 0.0625 & 0.00004 & 1269.41 & 730.44 & 2811.29 \\ \hline
100–120 & 0.00002 & 0.0003 & 0.0625 & 0.00004 & 1694.36 & 734.22 & 3846.79 \\ \hline
120–140 & 0.00002 & 0.0003 & 0.0625 & 0.0004 & 2668.45 & 1458.6 & 5853.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hybrid model performance based on CWL2 method. values. This shows that the model's predictions are generally very far from the fundamental values and that the model's learning process is ineffective. These results show that the JSMA attack is unsuitable for this model and dataset. The model cannot learn effectively on this dataset, and its predictions are often incorrect. For the model's predictions to become more accurate, the dataset's suitability may need to be improved, or the model may need to be given a deeper and more complex structure. As a result, the current model's performance on this dataset is relatively poor and it requires further development and improvement. The parameters used in the proposed attention mechanism + JSMA model are selected as epsilon = 0.001. This is a hyperparameter that determines how large the model parameters will become during optimization. In the JSMA attack, the lr parameter, which is the learning rate of the optimizer (AdamW), is also equal to this value. When Table 7 is examined, it is found that as the window size increases, the precision of the model generally decreases.