## 4 Results and Discussion
Figure 9: Performance results of FL-BDE, ML- and EL-based models for 5-fold cross-validation. In the literature, many studies have been conducted to detect malware through different classification methods by using static and dynamic analysis methods. Among them, artificial intelligence-based methods such as ML, DL and FL, as well as hybrid and EL-based approaches, have performed better in malware detection than the others have. A summary of the proposed model is presented in Table 5, along with information about the relevant studies in the literature and their performances in terms of the accuracy, recall, precision, F-measure and AUC metrics. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No.** & **Author** & **Method(s)** & **Dataset** & **Acc (\%)** & **Prec (\%)** & **Rec (\%)** & **F-m (\%)** & **AUC (\%)** \\ \hline
1 & [PERSON] and [PERSON] [24] & CNN &
\begin{tabular}{c} [PERSON] and \\ CICMalDroid \\ \end{tabular} & - & 96.03 & 96.3 & 96.08 & - \\ \hline
2 & [PERSON] et al. [27] & \
\begin{tabular}{c} Bi-Level \\ Optimization \\ \end{tabular} &
\begin{tabular}{c} [PERSON] and \\ AAGM \\ \end{tabular} & 98.18 & 98.06 & 98.34 & 97.79 & 86.80 \\ \hline
3 & [PERSON] et al. [22] & [PERSON] &
\begin{tabular}{c} [PERSON] and \\ AndroZoo \\ \end{tabular} & 94 & 91.1 & - & 91 & 96.75 \\ \hline
4 & [PERSON] et al. [26] & Contrastive Learning &
\begin{tabular}{c} [PERSON] and \\ AMGP \\ \end{tabular} & 97.53 & 96.66 & 98.41 & 97.53 & - \\ \hline
5 & [PERSON] et al. [30] &
\begin{tabular}{c} Fuzzy-Integral \\ Based Ensemble \\ approach \\ \end{tabular} & [PERSON] & 95.08 & 92.40 & 94.63 & 93.50 & 95.0 \\ \hline
6 & \begin{tabular}{c} [PERSON] \\ et al. [18] \\ \end{tabular} & \
\begin{tabular}{c} Fuzzy AHP \\ Analytical Hierarchy \\ Process) \\ \end{tabular} &
\begin{tabular}{c} [PERSON] and \\ AndroZoo \\ \end{tabular} & 90.54 & - & - & - & - \\ \hline
7 & \
\begin{tabular}{c} [PERSON] \\ and [PERSON] [31] \\ \end{tabular} &
\begin{tabular}{c} A Multifaceted Deep \\ Generative \\ Adversarial \\ Networks approach \\ \end{tabular} & 96.2 & 95.1 & 94.6 & 94.7 \\ \hline \end{tabular}
\end{table} 
Table 5: A summary of the proposed model and the relevant studies in the literature and their performance results. Figure 10: ROC curves of FL-BDE, ML- and EL-based models for 5-fold cross-validation. When the studies in Table 5 were compared with each other in terms of the performance metric results, it was found that the studies based on EL, comparative learning and DL were highly successful at malware detection. In comparison to the relevant models, the FL-BDE model, which was proposed as a FIS-based dynamic ensemble approach in our work, demonstrated a more competitive performance. The bi-level malware detection (BMD) model proposed by [PERSON] et al. [27] and the Bi-LSTM model using comparative learning suggested by [PERSON] et al. [26] are the other approaches that have performance results that are similar to that of the FL-BDE model. The data in the table make it clearly evident that the proposed model outperformed both of the models in terms of all of the performance metrics. When the performance of the proposed model is compared with the performance of the other models shown in Table 5, it can be seen that it performed much better than these models did.