## 4 Performance Validation  
The proposed model is simulated using Python 3.6.5 tool on PC i5-8600k, GeForce 1050 Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings are given as follows: learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count: 50, and activation: ReLU. In this section, the ransomware classification performance of the OGCNN-RWD technique can be observed on a database comprising 840 samples [21] as represented in Table 1.  
The confusion matrix of the OGCNN-RWD technique is demonstrated in Figure 3. The outcomes ensure that the OGCNN-RWD system has properly recognized goodware and ransomware samples. For instance, on 100 epochs, the OGCNN-RWD technique identifies 359 goodware and 386 ransomware samples. Moreover, on 200 epochs, the OGCNN-RWD method identifies 372 goodware and 401 ransomware samples. Furthermore, on 300 epochs, the OGCNN-RWD method identifies 372 goodware and 408 ransomware samples. Lastly, on 500 epochs, the OGCNN-RWD approach identifies 417 goodware and 420 ransomware samples. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Number of Instances** \\ \hline Goodware & 420 \\ \hline Ransomware & 420 \\ \hline Total No. of Samples & 840 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the dataset. Figure 3: _Cont._  
In Table 2, the overall ransomware classification outcomes of the OGCNN-RWD technique are inspected in several epochs. The OGCNN-RWD technique properly recognized goodware and ransomware. For the sample, with 100 epochs, the OGCNN-RWD methodology obtained _an accu\({}_{bal}\)_ of 88.69%, \(sens_{y}\) of 88.69%, \(spec_{y}\) of 88.69%, \(F_{score}\) of 88.68%, and MCC of 77.54%. In the meantime, with 100 epochs, the OGCNN-RWD approach attained _an accu\({}_{bal}\)_ of 92.02%, \(sens_{y}\) of 92.02%, \(spec_{y}\) of 92.02%, \(F_{score}\) of 92.01%, and MCC of 84.25%. Finally, with 100 epochs, the OGCNN-RWD method achieved _an accu\({}_{bal}\)_ of 92.86%, \(sens_{y}\) of 92.86%, \(spec_{y}\) of 92.86%, \(F_{score}\) of 92.84%, and MCC of 86.03%. Also, with 100 epochs, the OGCNN-RWD method reached an \(accu_{bal}\) of 99.52%, \(sens_{y}\) of 99.52%, \(spec_{y}\) of 99.52%, \(F_{score}\) of 99.52%, and MCC of 99.05%. At last, with 100 epochs, the OGCNN-RWD method attained an \(accu_{bal}\) of 99.64%, \(sens_{y}\) of 99.64%, \(spec_{y}\) of 99.64%, \(F_{score}\) of 99.64%, and MCC of 99.29%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy\({}_{\text{bal}}\)** & **Sensitivity** & **Specificity** & **F-Score** & **MCC** \\ \hline \multicolumn{6}{c}{**Epoch—100**} \\ \hline Goodware & 85.48 & 85.48 & 91.90 & 88.31 & 77.54 \\ \hline Ransomware & 91.90 & 91.90 & 85.48 & 89.04 & 77.54 \\ \hline
**Average** & **88.69** & **88.69** & **88.69** & **88.68** & **77.54** \\ \hline \multicolumn{6}{c}{**Epoch—150**} \\ \hline Goodware & 88.10 & 88.10 & 93.57 & 90.58 & 81.79 \\ \hline Ransomware & 93.57 & 93.57 & 88.10 & 91.08 & 81.79 \\ \hline
**Average** & **90.83** & **90.83** & **90.83** & **90.83** & **81.79** \\ \hline \multicolumn{6}{c}{**Epoch—200**} \\ \hline Goodware & 88.57 & 88.57 & 95.48 & 91.74 & 84.25 \\ \hline Ransomware & 95.48 & 95.48 & 88.57 & 92.29 & 84.25 \\ \hline
**Average** & **92.02** & **92.02** & **92.02** & **92.01** & **84.25** \\ \hline \multicolumn{6}{c}{**Epoch—250**} \\ \hline Goodware & 88.57 & 88.57 & 95.71 & 91.85 & 84.50 \\ \hline Ransomware & 95.71 & 95.71 & 88.57 & 92.41 & 84.50 \\ \hline
**Average** & **92.14** & **92.14** & **92.14** & **92.13** & **84.50** \\ \hline \multicolumn{6}{c}{**Epoch—300**} \\ \hline Goodware & 88.57 & 88.57 & 97.14 & 92.54 & 86.03 \\ \hline Ransomware & 97.14 & 97.14 & 88.57 & 93.15 & 86.03 \\ \hline
**Average** & **92.86** & **92.86** & **92.86** & **92.84** & **86.03** \\ \hline \end{tabular}
\end{table} 
Table 2: Ransomware classifier outcome of OGCNN-RWD algorithm with distinct epochs. Figure 3: Confusion matrices of OGCNN-RWD system: (**a**–**i**) Epoch 100–500.