## 4 Results and Discussions  
The performance of the proposed Cu-DNNGRU is evaluated concerning other competitive classifiers, i.e., Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU, under a reasonable performance matrix equipped with accuracy precision, recall, and f-Score. DNN-GRU was able to effectively learn from the dataset, as evidenced by the results produced in terms of accuracy vs. loss, as shown in Figure 3. The validation results for the model were 0.025% validation loss and 99.39% validation accuracy. Moreover, on a comparative performance scale, the proposed Cu-DNNGRU projects a phenomenal performance by achieving an overall accuracy of 99.39%, 99.09% precision, 98.89% recall, and 99.21% F1-score as witnessed in Figure 4. We have further provided the class-wise detection rate of the proposed model against the other models in Table 5.  
The proposed Cu-DNNGRU is evaluated on a ten-fold cross-validation under an investigative variety of performance parameters such as accuracy, precision, recall, and F1-score. It can be seen in Table 6 that Cu-DNNGRU has achieved remarkable performance in comparison with Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. Regarding the accuracy, the DNNGRU maintains the first-fold accuracy of 98.61%, 98.21% precision, 99.65% recall, and 98.99% F1-score. The progression continues in almost the same fashion until the 10 th fold, where Cu-DNNGRU has a projected accuracy of 99.92%, 98.71% precision, 99.12% recall, and 99.81% F1-score. \begin{table}
\begin{tabular}{c c} \hline
**Processor** & **I7 (3.33 GHz)** \\ \hline Generation & 8 th \\ \hline Libraries & Numpy, Tensorflow, Pandas, Keras, and Scikitlearn \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont_. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Cu-DNNGRU** & **Cu-LSTMDNN** & **Cu-BLSTM** & **Cu-GRU** \\ \hline Normal & 99.92\% & 99.15\% & 98.96\% & 98.69\% \\ \hline Mirai Scan & 99.86\% & 97.79\% & 97.29\% & 98.51\% \\ \hline Mirai UDP & 98.89\% & 98.62\% & 97.61\% & 98.12\% \\ \hline Mirai Ack & 99.71\% & 97.43\% & 98.10\% & 98.68\% \\ \hline Mirai SYN & 99.68\% & 98.81\% & 97.62\% & 98.25\% \\ \hline Mirai UDP Plain & 99.14\% & 98.89\% & 97.83\% & 97.89\% \\ \hline Gafgyt Junk & 98.61\% & 96.61\% & 97.26\% & 98.64\% \\ \hline Gift Combo & 99.85\% & 97.90\% & 98.15\% & 97.68\% \\ \hline Gafgyt TCP & 99.12\% & 97.36\% & 97.25\% & 98.36\% \\ \hline \end{tabular}
\end{table} 
Table 5: Class-wise detection accuracy. Figure 4: Accuracy, precision, recall, and F1-score analysis. Figure 3: Validation accuracy vs. validation loss. Accommodating a broader variety of assessment metrics, comprising the True Positive Rate (TPR), True Negative Rate (TNR), and Matthews Correlation Coefficient (MCC), the proposed Cu-DNNGRU is evaluated in comparison with Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. Figure 5 depicts that Cu-DNNGRU has shown a TPR of 99.12%, which is significantly exceptional compared to other competitive schemes. Moreover, in the case of TNR, Cu-DNNGRU again advertised an admirable performance with a TNR value of 98.86%. The relevant sequence exclusively goes on when the proposed CU-DNNGRU deliberates an exceptional MCC value of 98.15%. We have further investigated the performance of the proposed Cu-DNNGRU on rational performance metrics, including False Positive Rate (FPR), False Negative Rate (FNR), False Detection Rate (FDR), and False Omission Rate (FOR). It can be seen in Figure 6 that the proposed Cu-DNNGRU has achieved the FPR of 0.00293%, and the number is