## 5 Discussion
### Comparison of the Research Findings  
Table 9 provides a comparative analysis of previous research studies with respect to the performance of dataset utilization. The comparison study displays the performance metrics of various algorithms, such as LSTM, CNN-LSTM, and CNN, on the CICIDS2017 dataset as well as a specialized \"Store-and-forward\" dataset. These algorithms include CNN, CNN-LSTM, and LSTM. Accuracy, precision, and false positive rate (FPR) are the measures that are utilized during the review process. When interpreting the findings, it is helpful to focus on the findings of \"This Study\" on both datasets because they provide a significant amount of knowledge. The CNN-LSTM models (Studies [50; 53], and \"This Study\") indicate high accuracy on a consistent basis, with values that are greater than 99%. These models have performed admirably in terms of producing accurate forecasts for the vast majority of the cases contained in the CICIDS2017 dataset. In addition, the accuracy of the CNN model, as measured by Study [51], is rather high, coming in at 97.07%. Studies [50; 53], and \"This Study\" all report that the CNN-LSTM models attained precision values greater than 99%, which indicates a very low rate of false positives. Because these models have shown a high level of confidence in their positive predictions, they are appropriate for reducing the number of false alarms that occur during the process of intrusion detection. With values of approximately 97% and 77%, respectively, the CNN model (Study [51]) and the LSTM model (Study [49]) both have precision that is somewhat lower but still substantial. The CNN-LSTM models (Studies [53] and \"This Study\") have achieved extremely low FPR values of 0.1% and 0%, respectively. These results are presented in the table below. This indicates that they have been successful in minimizing the frequency of false positive predictions, which makes them extremely dependable for the detection of intrusions. The FPR for the LSTM model (Study [49]) and the CNN model (Study [51]) are higher but are still considered to be rather low, with values of 0.18% and 0.87%, respectively. Both studies \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Study** & **Algorithm** & **Dataset** & **Accuracy (\%)** & **Precision (\%)** & **FPR (\%)** & \\ \hline
[51] & LSTM & CICIDS2017 & 94.11 & 77.07 & 0.18 \\ \hline
[52] & CNN-LSTM & CICIDS2017 & 99.7 & 99.6 & / \\ \hline
[53] & CNN & CICIDS2017 & 97.07 & 97.14 & 0.87 \\ \hline
[54] & CNN-LSTM & CICIDS2017 & 95.6 & 97.6 & / \\ \hline
[55] & CNN-LSTM & CICIDS2017 & 99.64 & 99.7 & 0.1 \\ \hline This Study & CNN-LSTM & CICIDS2017 & 97.93 & 97.46 & 0 \\ \hline This Study & CNN-LSTM & Store-and-forward & 99.63 & 99.92 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of previous research studies. were conducted in the United States. In general, the CNN-LSTM models have exhibited greater performance in intrusion detection on the CICIDS2017 dataset compared to the separate CNN and LSTM models. This is the case regardless of which dataset is being used. They have repeatedly demonstrated great levels of accuracy and precision, as well as minimal rates of false positives. The CNN-LSTM technique has been shown to be effective, as evidenced by the fact that the CNN-LSTM model used in the most recent study (This Study) obtained performance that was comparable to that of earlier CNN-LSTM models (Studies [50; 53]). It is important to note that the results of the most recent study (This Study) are extremely similar to those of the studies that have the best-performing CNN-LSTM models (Studies [50; 53]). The models may have slight variations in their hyperparameters, architecture, or other experimental conditions, which could account for the little discrepancies in the levels of accuracy and precision that exist between them.