## 4 Conclusions  
In this paper, an FT-CFNN is proposed for malware image classification. This network comprises two parts: a CFNN and Taguchi-method-based optimal parameter combinations. Four feature fusion methods (GMP, GAP, CGMP, and CGMP) are proposed for the CFNN to reduce the feature size and integrate network information. An FNN is used to replace a fully connected network to reduce the size of adjustable parameters. The proposed CFNN achieved accuracy values of 96.68%, 97.86%, 98.13%, and 97.64% with the GMP, GAP, CGMP, and CGMP fusion methods, respectively, for the Malimg dataset. The experimental results indicated that the CFNN achieved the highest accuracy with the CGAP fusion method. To reduce the number of experiments for parameter combinations, the Taguchi method was used to determine the affecting factors and levels. The experimental results indicated that the accuracy of the FT-CFNN, proposed CFNN, and original LeNet model in malware family classification was 98.61%, 98.13%, and 96.68%, respectively.  
Because the Malimg dataset only contains 9339 images, generative adversarial networks can be used in the future to increase the number of images and thus improve stability. Indicators such as accuracy, sensitivity, and specificity have been widely used by scholars for comparing the classification and identification performance of deep learning networks. However, these parameters have different values in each network learning training step; therefore, the use of a single value or index average to assess a network's image classification performance might lead to inaccurate assessments. Therefore, future research should address this problem by using statistical methods to define a performance recognition index. Furthermore, in this study, we only use the existing malware database for analysis and classification, and do not apply the proposed method to actual cyberattack problems. Therefore, we will also apply the proposed method to practically any malicious programs that steal crucial data in future research.  
Conceptualization, C.-J.L. and M.-S.H.; Methodology, C.-J.L., M.-S.H. and C.-L.L.; Software, C.-J.L. and M.-S.H.; Data Curation, C.-J.L. and C.-L.L.; Writing--Original Draft Preparation, C.-J.L. and C.-L.L.; Supervision, C.-J.L.; Funding Acquisition, C.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 110-2221-E-167-031-MY2.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{1}{c}{**Methods**} & **Fusion Method** & **Accuracy** & **Parameters** \\ \hline [PERSON] and [PERSON] II [22] & 95.62\% & \textless{}1000 \\ \hline [PERSON] et al. [23] & 97.95\% & \textless{}1000 \\ \hline [PERSON] et al. [24] & 97.18\% & \textless{}1000 \\ \hline [PERSON] et al. [25] & 97.05\% & 5,408,561 \\ \hline [PERSON] et al. [26] & 98.52\% & 134,362,969 \\ \hline [PERSON] et al. [27] & 93.37\% & 555,329 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Proposed \\ method \\ \end{tabular} } & GAP & 96.68\% & 29,209 \\ \cline{2-4}  & GMP & 97.86\% & 29,209 \\ \cline{2-4}  & CGAP & 98.13\% & 22,681 \\ \cline{2-4}  & CGMP & 97.64\% & 22,681 \\ \cline{2-4}  & FT-CFNN & CGAP & 98.61\% & 13,609 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy values obtained by various methods and the number of parameters required by these methods.