## 3 Results and Discussion
### Class Incremental Learning Method
The metrics extracted by each family are presented in Appendix A, Table 16 presents the metrics obtained by Model 1 for the initial training without the [PERSON] family, Table 17 presents the results obtained by Model 1 after the first stage of incremental training, with examples of the new Metastealer class, Table 18 presents the results after the second training stage, and Table 19 shows the results of Model 1 trained exclusively on the reduced dataset. Separating the Metastealer family, with 80,000 examples available, to be used as a new class, the initial average accuracy obtained by the model was 86.53% and at the end of the second stage of incremental training, with the new class added to the model, it was 81.27%; therefore, there was a loss of accuracy of 5.27%, but 22.07% higher compared to the accuracy of the model trained without transfer learning with the reduced set of examples. The recall in the initial stage of the model with 60 families was 80.54% and rose to 84.33% after the second stage of incremental training, a gain in this metric of 3.79%, and a gain of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Train Stage** & **Avg. Prec.** & **Avg. Rec.** & **Avg. F1** \\ \hline Initial & 86.72\% & 82.53\% & 82.79\% \\
1 st Incremental & 78.23\% & 79.24\% & 76.03\% \\
2 nd Incremental & 82.00\% & 84.88\% & 81.51\% \\ From Scratch & 57.16\% & 73.23\% & 60.38\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average of the metrics obtained by the proposed Model 1 during the incremental process using Suppobox as an incremental class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Train Stage** & **Avg. Prec.** & **Avg. Rec.** & **Avg. F1** \\ \hline Initial & 86.52\% & 81.67\% & 82.77\% \\
1 st Incremental & 80.85\% & 85.15\% & 81.53\% \\
2 nd Incremental & 81.56\% & 85.98\% & 82.45\% \\ From Scratch & 62.03\% & 75.15\% & 65.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average of the metrics obtained by the proposed Model 1 during the incremental process using Darkshell as an incremental class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Train Stage** & **Avg. Prec.** & **Avg. Rec.** & **Avg. F1** \\ \hline Initial & 86.53\% & 80.54\% & 81.76\% \\
1 st Incremental & 80.86\% & 84.21\% & 81.04\% \\
2 nd Incremental & 81.27\% & 84.33\% & 81.27\% \\ From Scratch & 59.19\% & 73.96\% & 62.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average of the metrics obtained by the proposed Model 1 during the incremental process using Metastealer as an incremental class. 10.38% compared to the trained model with no transfer of learning. The F1 score in the initial stage was 81.76% and became 81.27% at the end of the second stage of incremental class training, which is a loss of 0.49%, but a gain about the model trained from scratch without the incremental process of 18.76%. When it was separated from the Suppobox family, with 10,350 examples in the dataset, as an incremental class, the initial average accuracy obtained by the model was 86.72% and at the end of the second step of incremental training, with the new class added to the model, it was 82.00%, therefore showing a loss of accuracy of 4.72%, but 24.84% higher compared to the accuracy of the model trained without transfer learning with the reduced set of examples. The recall in the initial stage of the model with 60 families was 82.53% and rose to 84.88% after the second stage of incremental training, showing a gain in this metric of 2.35% and a gain of 11.65% compared to the trained model without transfer learning. The F1 score in the initial stage was 82.79% and became 81.51% at the end of the second stage of incremental class training, showing a loss of 1.28%, but a gain in the model trained from scratch without the incremental process of 21.13%.