## 4 Model Evaluations
### Model Evaluations on Combination of CNN with LSTM  
The long short-term memory (LSTM) unit demonstrates proficiency in effectively capturing the temporal dependencies among packets. This observation was made in the previous analysis, indicating that it exhibits superior performance compared to CNNs. For this reason, the present study aims to integrate both the convolutional neural network \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Acc** & **Recall** & **Precision** & **FPR** & **F1-Score** \\ \hline Normal & 92.62 & 88.35 & 90.57 & 0.11 & 86.24 \\ \hline Flow Duration Modification & 89.38 & 90.82 & 87 & 0.14 & 90.15 \\ \hline Forward Packet Modification & 91.56 & 91.25 & 91.58 & 0.10 & 89.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance training with CNN on store-and-forward dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Acc** & **Recall** & **Precision** & **FPR** & **F1-Score** \\ \hline Normal & 97.48 & 96.91 & 96.06 & 0 & 91.21 \\ \hline Flow Duration Modification & 91.18 & 98.62 & 92.78 & 0 & 93.24 \\ \hline Forward Packet Modification & 93.44 & 97.73 & 96.23 & 0.16 & 91.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance training with LSTM on store-and-forward dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Acc** & **Recall** & **Precision** & **FPR** & **F1-Score** \\ \hline Normal & 97.46 & 90.28 & 85.62 & 0.14 & 90.11 \\ \hline Flow Duration Modification & 89.29 & 84.53 & 91.69 & 0.12 & 85.23 \\ \hline Forward Packet Modification & 84.67 & 88.64 & 89.52 & 0.15 & 91.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model performance training with CNN on CICIDS2017 dataset. (CNN) and long short-term memory (LSTM) models in order to analyze the accuracy of their combined performance (see Table 7). The motivation behind integrating convolutional neural networks (CNN) and long short-term memory (LSTM) into a hybrid model is to capitalize on the unique advantages offered by each architecture while mitigating their respective limitations. The integration of both convolutional neural networks (CNNs) and long short-term memory (LSTM) models in this approach enables the effective capture of spatial patterns from the CNN and temporal dependencies from the LSTM. Consequently, this model is well-suited for tasks that involve sequential data, such as time series analysis or sequence classification. Through careful examination of two distinct datasets, the analysis conducted on the CICIDS2017 dataset reveals that the utilization of CNN or LSTM architectures in the model showcases superior performance. This enhanced performance can potentially be attributed to the temporal characteristics inherent in the data. Therefore, a significant number of accuracies were observed, indicating that the prediction of feature modifications can be effectively predicted using a combined hybrid approach. The interpretation of model performance in the training of a convolutional neural network (CNN) combined with long short-term memory (LSTM) on the CICIDS2017 dataset using receiver operating characteristic (ROC) analysis entails comprehending the accuracy and efficiency of the analysis, while also considering potential uncertainties. Consequently, the receiver operating characteristic (ROC) curves, depicted in Figure 3, demonstrate that the experiment approached a state of near perfection, as evidenced by the prominently rising curve converging towards unity. Therefore, the performance of the CNN + LSTM model in predicting tasks on the CICIDS2017 dataset shows a tendency towards improvement. The hybrid CNN-LSTM model is an effective architecture for store-and-forward data, as it offers a strong framework for capturing spatial and temporal patterns. The selection of the depth and architecture of the CNN and LSTM layers was customized to suit the distinct attributes of the data and the demands of the store-and-forward data task.