## 3 Proposed Visualized Malware Multi-Classification Framework  
Effective detection of windows malware families is a mandatory aspect in Internet security applications. Rather than employing the conventional features extraction-based ML approaches that require high computational processing in texture analysis, in this paper, efficient visualized malware multi-classification models are introduced based on deep learning (DL) approaches. Therefore, to efficiently identify malware images with reduced computations and achieve maximum identification accuracy, different fine-tuned CNN-based TL models are developed and used, as shown in the proposed malware multi-classification framework in Figure 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Work** & **The Aim** & **Classification Approach** & **Model** & **Input** & **Accuracy** \\ \hline
[5] & To propose a malware detection solution that handles imbalanced data & Deep learning & DenseNet & gray-scale images & 97.55\% \\ \hline
[6] & To develop a deep forest model by implementing a layered ensemble approach & Deep Random Forest & Sliding Window & gray-scale images & 98.65\% \\ \hline
[7] & To develop a visualized malware classification system based on ANN & ANN & Scratch model & file properties, gray-scale image & 99.135\% \\ \hline
[8] & To improve the accuracy of malware detection by combining AlexNet and Inception-v3 techniques & DCNN, SFTA & AlexNet & gray-scale images & 99.3\% \\ \hline
[9] & To implement a lightweight CNN to classify malware images & Lightweight CNN & Scratch model & gray-scale images & 97.68\% \\ \hline
[29] & To develop a file agnostic deep learning scheme to effectively group malware into families based on their patterns & CNN & Scratch model & gray-scale images & 98.4\% \\ \hline
[31] & To implement a CNN classification system based on colored images of malware & CNN & VGG16, ResNet50 & colored images & 98.82\% \\ \hline
[34] & To develop an accurate and fast classification system by integrating CNN with Gradient Boosting algorithm & CNN & XG-Boost & gray-scale images & 98.7\% \\ \hline
[32] & To combine deep CNN with an entropy graph to enhance the malware pattern classification process & CNN & Scratch model & gray-scale images & 99.67\% \\ \hline
[33] & To implement an industrial Internet of things malware classification system based on colored image visualization & DCNN & Scratch model & colored images & 98.79\% \\ \hline
[35] & To implement a visualization-based malware detection system using VGG16 and ResNet50 CNN models & CNN & VGG16, ResNet50 & gray-scale images & 98.11\% \\ \hline
[38] & To perform malware classification by deploying a sequential multilayered Random Forest ensemble technique & ML & RF, Xgboost, ETC, LR & gray-scale images & 98.91\% \\ \hline
[39] & To classify malware images using an image feature descriptor to extract the similarities among them & ML & KNN & gray-scale images & 97\% \\ \hline
[40] & To deploy the local and global malicious patterns in the malware classification process & ML & LGMP & gray-scale images & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison among the recent related works that have employed Malimg in their studies.  
The used fine-tuned CNN models do not use reverse engineering for the malware multi-classification process. The basic procedure of the proposed malware multi-classification framework is shown in Figure 1. It consists of five different phases: (1) Dataset preparation, (2) Pre-processing, (3) Transfer learning, (4) Fine-tuning and classification, and (5) Performance evaluation. The details of these phases are as follows:  
Figure 1: Proposed visualized malware multi-classification framework.