## 4 The Proposed Methods
Its foundation is the idea of ensemble learning, which is the process of combining multiple classifiers in order to address a difficult problem and improve the functioning of the model. RF is a machine learning method based on decision trees. It can handle huge datasets that are highly dimensional. It improves the model's accuracy and avoids an overfitting problem [45]. Step 4.6: Extreme Learning Machines (ELM) Classifier. The simple connections in an ELM [46] make it a design that is considered uncomplicated. It eliminates the issues of overfitting and slow training speed as compared to the traditional neural network learning technique. The empirical risk reduction theory forms the basis of ELM, and its learning process requires just one iteration. Consequently, it does a great job of forecasting performance in specific fields. Given the abundance of newly discovered malware variations, ELMs are appealing for malware classification. ## 5 Results and Discussion

This section is divided into four parts: public datasets, the metrics of performance evaluation, evaluation conclusions, and comparison with different methodologies. The experiment was conducted depending on some of the specifications. See Table 3 for additional details. ### Datasets

The Malimg dataset [47] was employed to evaluate the effectiveness of the proposed approach. The Malimg dataset contains an imbalance of 9339 malware images, which were collected across 25 different families and classes. Table 4 shows that every sample in the dataset belongs to one of the 25 malware families. Malware images may have varying dimensions. The size and texture of these datasets are similar. A classifier can easily accomplish the task. Variants of several malware families have been shown to be extremely distinctive when compared with various other datasets. An efficient malware detection software with the ability to differentiate between malware and malicious files was found. It is therefore evident that the benign files class is not included. Therefore, it is inappropriate to investigate the proposed approach using the database as it is now configured. Thus, the researcher added an additional kind of benign input to the database by gathering files that are not harmful (350 benign files) sourced from the MaleVis dataset. The Malimg dataset provides an extensive amount of train and test datasets that we can use, which makes it ideal for use in the CNN model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**DB**} & \multirow{2}{*}{**Class ID**} & \multirow{2}{*}{**Family**} & \multicolumn{2}{c}{**Details**} \\ \cline{3-4}  & & & **Malware Category** & **Sample No.** \\ \hline \multirow{4}{*}{Malimg} & \#1, \#9, \#12 & \begin{tabular}{c} Adialer.C, Dialplatform.B, \\ Instantaccess \\ \end{tabular} & Dialer & 122, 177, 431 \\ \cline{2-4}  & \#2, \#19 & Agent.FYI, Robotgen, & Backdoor & 116, 158 \\ \cline{2-4}  & \#3, \#4, \#6, \#23, \#25 & \begin{tabular}{c} Allaple.A, Allaple.L, Autorun.K, \\ VB.AT, Yuner.A \\ \end{tabular} & Worm & 2949, 1591, 106, 408, 800 \\ \cline{2-4}  & \#5, \#7, \#8, \#17, \#20 & \begin{tabular}{c} Alueron.gen1/C2 LOP.P, \\ C2 LOP.gen1/g, Malex.gen1/, \\ Skintrim.N, \\ \end{tabular} & Trojan & 198, 200, 146, 80, 136 \\ \cline{2-4}  & \#11 & \begin{tabular}{c} Fakerean \\ \end{tabular} & rogue & 381 \\ \cline{2-4}  & \#10, \#18, \#21, \#22, \#24 & \
\begin{tabular}{c} Dontovo.A, Obfuscator.AD, \\ Swizzor.gen1/E, Swizzor.gen1/, \\ Wintrim.BX \\ \end{tabular} & Downloader & 162, 142, 128, 132, 97 \\ \cline{2-4}  & \#13, \#14, \#15, \#16 &
\begin{tabular}{c} Lolyda.AA1, Lolyda.AA2, \\ Lolyda.AA3, Lolyda.AT \\ \end{tabular} & PWS & 213, 184, 123, 159 \\ \cline{2-4}  & \multicolumn{2}{c}{Total} & - & 9339 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the Malimg dataset families.