## 5 Results and Discussion  
### Results and Analysis  
This section presents the results and analysis for model implementation. We used Jupyter Notebook software with the Python programming language. We used the Keras and scikitlearn packages for data pre-processing and implementing the proposed model. We trained the proposed model on a MacBook Air with an Intel Core i5 CPU 1.6 GHz processor and 8 GB RAM. Additionally, we implemented different state-of-the-art ML models on the datasets to compare their performance with that of our proposed model. Figure 4 presents the confusion matrix of our proposed model on the BoT-IoT dataset. The results show that the model correctly classified most of the cyber threat categories. Furthermore, to illustrate the quality of the proposed model, the receiver operating characteristic (ROC) curve is plotted in Figure 5 for the BoT-IoT dataset. Figure 6 presents the confusion matrix of our proposed model on the TON_IoT dataset, and the ROC curve is presented in Figure 7. Both ROC curves show that our proposed model achieved the highest value of 1. Thus, our proposed model performed very well with all the classes. The results of our proposed model on the testing datasets are presented in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Accuracy\%** & **TPR\%** & **FPR** \\ \hline BoT-IoT & 99.99 & 99.92 & 0.0003 \\ \hline TON\_IoT & 99.99 & 99.99 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of cyber threat classification on both datasets. Figure 4: Confusion matrix based on the BoT-IoT dataset. Figure 5: ROC curve of using our proposed model on the BoT-IoT dataset. As shown in Table 4, the proposed model achieved high accuracy, with an average of 99.99% on both datasets. The TPR reached averages of 99.92% with the BoT-IoT dataset and 99.99% with the TON_IoT dataset. The proposed model achieved a low FPR of 0.0003 with the BoT-IoT dataset and 0.001 with the TON_IoT dataset. Thus, the proposed model showed good performance in classifying the threats with both datasets. Moreover, to demonstrate the effectiveness of the QRNN, we implemented our proposed model with LSTM instead of the QRNN to compare performance. Cybersecurity threats are very critical [54; 55; 56], and the results shown in Tables 5 and 6 highlight that our proposed approach could be very effective in dealing with them. According to the results in Tables 5 and 6, our proposed model with the QRNN showed the same performance as our proposed model with LSTM in terms of accuracy, precision, recall, and F-Score. In terms of time, the proposed model with the QRNN showed better performance for training the model and testing. The average training time per epoch demonstrated that the QRNN performed faster than LSTM in terms of training the model on both datasets, with a 418.3 s difference on the BoT-IoT dataset and a 19.8 s difference on the TON_IoT dataset. Additionally, for the classification time on the test dataset, the QRNN model performed faster than LSTM, with a 75 s difference on the BoT-IoT dataset and a 3 s difference on the TON_IoT dataset. The QRNN showed its effectiveness in increasing the speed of the model while providing a high accuracy and low FPR. Therefore, the model can be used for real-time CTI. We further compared the performance of our proposed model on the BoT-IoT and TON_IoT datasets against the state-of-the-art models for the multi-class classification of threats. The results of these comparisons are shown in Tables 7 and 8. As shown in Tables 7 and 8, though K-NN [19] and RF [28] showed good performance for recall and F-score on the BoT-IoT dataset, our proposed model outperformed the state-of-the-art models on both datasets. Additionally, we implemented different ML models to compare their performance with that of our model. The accuracy, TPR, and FPR values of each model are given are Tables 9 and 10.