## 4 The Proposed Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author/Year** & \
\begin{tabular}{c} **Feature Extraction** \\ **Method** \\ \end{tabular} & **Classifier** & **Dataset** &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \hline Makandar and Patrot, & & & & \\
2017 [50] & Gabor wavelet & KNN & Malimg & 89 \\ \hline [PERSON] et al., 2017 [51] & ResNet-50 & SoftMax & Malimg & 98 \\ \hline [PERSON] et al., 2018 [52] & LBP & SVM & Malimg & 75 \\ \hline [PERSON] et al., 2018 [14] & GoogLeNet & SoftMax & & 74 \\ \hline [PERSON] et al.,2019 [53] & LBP & KNN & Malimg & 91 \\ \hline [PERSON] et al., 2019 [54] & ResNet-50 & KNN & Malimg & 94 \\ \hline [PERSON] et al., 2020 [55] & VGG16 & DCNN & Malimg & 97 \\ \hline [PERSON] et al., 2020 [17] &
\begin{tabular}{c} VGG16, RESNET50, \\ INCPETIONV3 \\ \end{tabular} & CNN & Malimg & 97 \\ \hline [PERSON] et al., 2021 [56] & DCT & CNN & MaleVis & 96 \\ \hline [PERSON] et al., 2023 [57] & ResNet-50 & CNN & Malimg & 81 \\ \hline DenseNet201-KNN (Proposed) & DenseNet201 & KNN & Malimg & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparing performance with previous approaches. ## 6 Conclusions

The main aim of the proposed is to identify and determine the best features and classifiers and then use it in order to propose the malware detection method. According to the comparison findings, KNN is the best machine learning classifier. It outperformed the other classifiers on the Malimg datasets in terms of both accuracy and error. The GDA classifier came in second place. The ELM classifier comes in last and yields the worst results. In addition, DenseNet201 is the best pretrained model under the Malimg dataset. Therefore, the proposed DenseNet201-KNN method had an accuracy rate of 96% and a minimal error rate of 3.07%. The proposed methods surpass existing state-of-the-art approaches. The proposed feature extraction is computationally quicker than most other methods since it uses a lightweight design and fewer feature vector dimensions. Therefore, the proposed method might work well for implementation on small electronic devices. Nonetheless, additional space is required to increase accuracy. Therefore, in the future, a comparison study of a wide range of deep learning models will be needed. It is feasible to lessen the issue of imbalanced datasets and improve the efficacy of the proposed malware detection technique by balancing and expanding the dataset. Conceptualization, I.T.A., B.T.H. and F.A.A. ; writing--original draft preparation, I.T.A. ; writing--Original Draft Preparation, B.T.H. ; review and editing, F.A.A. ; Validation, B.T.H. ; Software, I.T.A. ; Validation, F.A.A. ; Funding acquisition, I.T.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original data presented in the study are openly available in [47]. The authors declare no conflicts of interest. ## References

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