## 5 Results and Discussion
### Comparison with Alternative Methods  
To demonstrate the proposed method's efficacy in identifying and classifying malware even when applied to identical datasets with unbalanced malware families, its performance is compared with a number of well-known methods that rely on hand-crafted features and deep Features. Most comparison techniques have been tested using the Malimg datasets. In  
Figure 14: The classification accuracy based deep feature method across three classifiers. terms of the accuracy metric, Table 4 displays the performance comparison of the proposed multi-classification framework employing Tamura, the GoogleNet CNN model, and three classifiers. Comparison of the proposed method to existing MC methods, which employ various hardware and software. Therefore, we use the outcomes of methods that employ the Malimg database, various feature domains, different texture feature extraction, and classification techniques. After incorporating Tamura and ELM, the proposed classification accuracy increased to 95.42 percent, which is higher than that of conventional hand-crafted feature methods. The accuracy results of the methods [37; 38] can be near to the presented method's optimal accuracy. However, these methods rely on GIST texture features, which demand huge feature vector dimensions. In contrast, our approach only needed a 1 \(\times\) 3 feature vector. After incorporating GoogLeNet and KNN, the proposed classification accuracy increased to 96.94 percent, which is higher than that of conventional deep Feature methods. The technique [39] may have the highest overall accuracy out of all the techniques. However, the combined feature of Pretrained VGG-16, ResNet-50, and AlexNet models involves greater computation time. The accuracy outcomes of the approaches [40; 41; 42] may be close to the best accuracy of the method described. As a result, the pre-trained feature of the VGG16 model displays impressive resilience. Unfortunately, processing these features took additional time. On the other hand, we attain high accuracy by exploiting the features of a pre-trained GoogleNet model. It is important to note that [19] is the least efficient method available. The usage of the end-to-end GoogleNet CNN model is the cause of this. Because of their whole reliance on full CNN, the techniques [12; 43] give rather moderate results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Method** & **Methods** & **Feature Extraction** & **Classifier Kind** & **Dataset** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{Handcrafted Features} & [PERSON] et al. [37] & Texture features & Random Forest & Malimg & 95 \\ \cline{2-6}  & [PERSON] et al. [44] & Sparse binary features & Neural networks and logistic regression & Malimg & 86 \\ \cline{2-6}  & [PERSON] et al. [38] & GIST & SVM & Malimg & 92.20 \\ \cline{2-6}  & [PERSON] \& Patrot [8] & Gabor wavelet & KNN & Malimg & 89.11 \\ \cline{2-6}  & [PERSON] et al. [45] & LBP & SVM & Malimg & 75.02 \\ \cline{2-6}  & **Proposed (Tamura + ELM)** & [PERSON] & ELM & Malimg & 95.42 \\ \hline \multirow{8}{*}{Deep Features} & [PERSON] et al. [40] & static and & & & \\  & & dynamitic analysis & SVM & Malimg & 95.2 \\ \cline{2-6}  & [PERSON] and [PERSON], [39] &
\begin{tabular}{c} VGG-16, \\ ResNet-50 and \\ AlexNet \\ \end{tabular} & CNN & Malimg & 98.88 \\ \cline{2-6}  & [PERSON] et al. [43] & CNN & MLP & Malimg & 85 \\ \cline{2-6}  & [PERSON] et al. [42] & VGG16 & Naive Bayes & Malimg & 91.72 \\ \cline{2-6}  & [PERSON] et al. [12] & CNN & SVM & Malimg & 84 \\ \cline{2-6}  & [PERSON] et al. [41] & VGG16 & SVM & Malimg & 92.29 \\ \cline{2-6}  & [PERSON] et al. [19] & GoogleNet & GoogleNet & Malimg & 74.5 \\ \cline{2-6}  & [PERSON] \& [PERSON] [12] & CNN & SVM & Malimg & 77.22 \\ \cline{2-6}  & **Proposed (DEEP + KNN)** & GoogleNet & KNN & Malimg & 96.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method to existing MC methods which were evaluated on the Malimg dataset.