## 3 The Proposed Model
### Parameter Optimization: DWO Algorithm
For instance, on run-1, the DWOML-RWD model attained an average \(accu_{y}\), \(sens_{y}\), \(spec_{y}\), and \(F_{score}\) of 98.69%, 98.69%, 98.69%, and 98.69%, respectively. Meanwhile, on run-2, the DWOML-RWD model gained an average \(accu_{y}\), \(sens_{y}\), \(spec_{y}\), and \(F_{score}\) of 98.69%, 98.69%, 98.69%, and 98.69%, respectively. Furthermore, on run-3, the DWOML-RWD model achieved an average \(accu_{y}\), \(sens_{y}\), \(spec_{y}\), and \(F_{score}\) of 99.17%, 99.17%, 99.17%, and 99.17%, respectively. Further, on run-4, the DWOML-RWD model acquired an average \(accu_{y}\), \(sens_{y}\), \(spec_{y}\), and \(F_{score}\) of 99.05%, 99.05%, 99.05%, and 99.05%, respectively. Lastly, on run-5, the DWOML-RWD model obtained an average \(accu_{y}\), \(sens_{y}\), \(spec_{y}\), and \(F_{score}\) of 99.40%, 99.40%, 99.40%, and 99.40%, respectively. Figure 5 demonstrates the detailed ransomware classification performance of the DWOML-RWD approach in terms of FNR and FPR. The results denote that the DWOML-RWD approach ensured enhanced performance under both classes. For example, on run-1, the DWOML-RWD model attained an average FNR and FPR of 1.31% and 1.31%, respectively. In parallel, on run-2, the DWOML-RWD model obtained an average FNR and FPR of 1.31% and 1.31%, respectively. Additionally, on run-3, the DWOML-RWD model gained an average FNR and FPR of 0.83% and 0.83%, respectively. Further, on run-4, the DWOML-RWD model reached an average FNR and FPR of 0.95% and 0.95%, respectively. Lastly, on run-5, the DWOML-RWD model obtained an average FNR and FPR of 0.60% and 0.60%, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** & **FNR** & **FPR** \\ \hline  & & & **Run-1** & & & \\ \hline Goodware & 98.69 & 98.10 & 99.29 & 98.68 & 01.90 & 00.71 \\ \hline Ransomware & 98.69 & 99.29 & 98.10 & 98.70 & 00.71 & 01.90 \\ \hline
**Average** & **98.69** & **98.69** & **98.69** & **98.69** & **01.31** & **01.31** \\ \hline  & & & **Run-2** & & & \\ \hline Goodware & 98.69 & 98.33 & 99.05 & 98.69 & 01.67 & 00.95 \\ \hline Ransomware & 98.69 & 99.05 & 98.33 & 98.70 & 00.95 & 01.67 \\ \hline
**Average** & **98.69** & **98.69** & **98.69** & **98.69** & **01.31** & **01.31** \\ \hline  & & & **Run-3** & & & \\ \hline Goodware & 99.17 & 98.81 & 99.52 & 99.16 & 01.19 & 00.48 \\ \hline Ransomware & 99.17 & 99.52 & 98.81 & 99.17 & 00.48 & 01.19 \\ \hline
**Average** & **99.17** & **99.17** & **99.17** & **99.17** & **00.83** & **00.83** \\ \hline  & & & **Run-4** & & & \\ \hline Goodware & 99.05 & 98.57 & 99.52 & 99.04 & 01.43 & 00.48 \\ \hline Ransomware & 99.05 & 99.52 & 98.57 & 99.05 & 00.48 & 01.43 \\ \hline
**Average** & **99.05** & **99.05** & **99.05** & **99.05** & **00.95** & **00.95** \\ \hline  & & & **Run-5** & & & \\ \hline Goodware & 99.40 & 99.05 & 99.76 & 99.40 & 00.95 & 00.24 \\ \hline Ransomware & 99.40 & 99.76 & 99.05 & 99.41 & 00.24 & 00.95 \\ \hline
**Average** & **99.40** & **99.40** & **99.40** & **99.40** & **00.60** & **00.60** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of DWOML-RWD technique with distinct measures and runs. The training accuracy (TRA) and validation accuracy (VLA) acquired by using the DWOML-RWD approach on the test dataset are exemplified in Figure 6. The experimental outcome represented by the DWOML-RWD technique attained higher values of TRA and VLA. Seemingly, the VLA is greater than TRA. Figure 4: Average analysis of DWOML-RWD approach with distinct measures. Figure 5: Average FNR and FPR analysis of DWOML-RWD approach with distinct runs. The training loss (TRL) and validation loss (VLL) attained by **using** the DWOML-RWD method on the test dataset are exhibited in Figure 7. The experimental outcome denotes the DWOML-RWD approach exhibited the lowest values of TRL and VLL. Particularly, the VLL is lower than the TRL. Figure 6: TRA and VLA analysis of DWOML-RWD approach. Figure 7: TRL and VLL analysis of DWOML-RWD approach.