## 5 Conclusions and Future Work  
There is an ongoing competition between anti-malware software and cyber-attackers' methods. Malware is one of the most widespread cyber-attacks on the Internet. Consequently, it is essential to continue building innovative, intelligent security tools to mitigate these security attacks. Thus, efficient artificial intelligence (AI) tools are designed and used to detect malicious software. Unfortunately, AI-based anti-malware solutions based on ML algorithms introduce considerable development costs by generating an extensive set of handcrafted features identification and extraction, which requires the time and expertise of data scientists and malware analysts. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multirow{2}{*}{\begin{tabular}{c} **No. of** \\ **Layers** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Storage** \\ **Requirement** \\ **(MB)** \\ \end{tabular} } & \begin{tabular}{c} **Total No. of** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Non-** \\ **Trainable** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Trainable** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Execution** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Avg. Time** \\ **per Malware** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Reduced** \\ **Training** \\ **Parameters (\%)** \\ \end{tabular} \\ \hline VGG16 & 16 & 515 & 138 & 137,897,600 & 102,400 & 660 & 0.705 & 99.92 \\ AlexNet & 8 & 227 & 61 & 60,897,600 & 102,400 & 2340 & 0.2505 & 99.83 \\ DarkNet53 & 53 & 155 & 41.6 & 41,574,400 & 25,600 & 4980 & 0.5331 & 99.93 \\ DenseNet-201 & 201 & 167 & 20 & 19,952,000 & 48,000 & 8160 & 0.8736 & 99.76 \\ InceptionV3 & 48 & 89 & 23.9 & 23,848,800 & 51,200 & 6420 & 0.6873 & 99.78 \\ Places365- & 22 & 27 & 7 & 6,974,400 & 25,600 & 2220 & 0.2377 & 99.63 \\ GoogleNet & 50 & 96 & 25.6 & 25,548,800 & 51,200 & 3420 & 0.3661 & 99.80 \\ MobileNetV2 & 53 & 13 & 3.5 & 3,468,000 & 32,000 & 3360 & 0.3597 & 99.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental specifications and execution time of the tested FT CNN-based TL models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc. (\%)** & **Prec. (\%)** & **Rec. (\%)** & **F1-Score (\%)** \\ \hline
[5] & 97.55 & 97.43 & 97.50 & 97.46 \\
[6] & 98.65 & 98.86 & 98.63 & 98.74 \\
[7] & 99.14 & - & - & - \\
[8] & 99.30 & - & 96.00 & 9.00 \\
[9] & 97.68 & 98.00 & 98.00 & 98.00 \\
[29] & 98.40 & - & - & 97.45 \\
[31] & 98.82 & 98.85 & 98.81 & 98.75 \\
[32] & 99.67 & 99.02 & - & - \\
[33] & 98.79 & 98.79 & 98.47 & 98.46 \\
[34] & 98.70 & - & - & - \\
[35] & 98.11 & 98.53 & 98.64 & 98.24 \\
[38] & 98.91 & 99.00 & 99.00 & 99.00 \\
[39] & 97.00 & 97.00 & 97.00 & 97.00 \\
[40] & 98.00 & 98.20 & 98.40 & 97.10 \\ Proposed & 99.97 & 99.04 & 99.01 & 99.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative analysis between the proposed multi-classification framework and related ML and DL-based malware multi-classification approaches tested on the Malimg dataset. On the other hand, AI-based anti-malware solutions based on DL algorithms and CNN architectures have exhibited outstanding performance in identifying malware quickly and efficiently. Therefore, this paper introduced a DL-based visualized malware multi-classification framework to classify different unbalanced families of malware images. This framework was built based on malware visualization, fine-tuning, and CNN-based transfer learning phases that were well-developed to accurately detect different categories of malware families. The proposed framework comprises eight fine-tuned CNN models, VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2, that were already pre-trained on the ImageNet database.