## 3 Methodology
### Feature Extraction
#### 3.4.1 Texture Feature (SFTA)
In order to get rid of the imbalanced data sets problem, we perform augmentation and balance the dataset and improve the performance of the proposed model. Malware classification based on deep learning introduced an extraordinary improvement when compared with the existing strategies and the experiment result proves the efficiency of the proposed technique as the accuracy was improved and model loss was decreased. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Year** & **Features** & **Techniques** & **Accuracy (\%)** \\ \hline [PERSON] et al. [51] & 2011 & GIST feature & Nearest Neighbor & 97.18 \\ [PERSON] et al. [52] & 2012 & Gaussian kernel features & SVM & 98.0 \\ [PERSON] et al. [53] & 2013 & Sparse binary features & Neural networks and logistic & 86.0 \\ [PERSON] et al. [54] & 2014 & Graph-based feature & Semantics-based & 93.0 \\  & & Echo state networks & & \\ [PERSON] et al. [55] & 2015 & (ESNs) and recurrent & Logistic regression and & 98.3 \\  & & neural networks (RNNs) & multilayer perceptron classifier & \\ [PERSON] [56] & 2016 & Texture features & Random Forest & 95.0 \\ [PERSON] et al. [57] & 2017 & Filter-based feature & Machine learning techniques & 99.0 \\ [PERSON] et al. [58] & 2017 & N-gram-based texture & Shared nearest neighbor (SNN) & 98.9 \\  & & feature & clustering algorithm & \\ [PERSON] et al. [59] & 2018 & Deep learning-based & Gradient boosting & 96.0 \\ [PERSON] et al. [60] & 2018 & GIST features & CNN-based architecture & 98.5 \\ [PERSON] et al. [61] & 2019 & Local and global malware & SVM, KNN & 98.0 \\ [PERSON] et al. [12] & 2019 & CNN-based features & CNN & 97.6 \\ [PERSON] et al. [62] & 2019 & Combined local and global & DCNN & 98.18 \\  & & malware (CLGM) features & & \\
**This paper** & **2020** & **Fused SFTA and deep network features** & **DCNN (AlexNet, Inception v3)** & **99.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with existing state-of-the-art algorithms. The proposed method achieved an accuracy of 99.3% on the cubic SVM classifier, which displays exceptional performance when compared to other current malware classification approaches. Conceptualization, M.R. and M.A.K. ; methodology, M.R. and M.A.K. ; software, M.N., J.H.S., S.K. and M.A.K. ; validation, M.N., J.H.S., S.K., M.R., M.A.K., and R.D. ; formal analysis, M.A.K., R.D. and T.B. ; investigation, M.N., J.H.S., S.K., M.R., M.A.K., and R.D. ; resources, M.N., J.H.S., S.K., M.R., and M.A.K. ; data curation, M.N., J.H.S., and S.K. ; writing--original draft preparation, M.R. and M.A.K. ; writing--review and editing, R.D. and T.B. ; visualization, M.N., J.H.S., S.K., M.R., M.A.K., and R.D. ; supervision, M.A.K. ; funding acquisition, T.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. ## References

* (1)[PERSON] (1978)The automated classification of malware detection using malware detection. In Proceedings of the 2018 ACM SIGKDD international conference on computer science and technology, pp. 1-10. Cited by: SS1. * (2)[PERSON] (1978)The automated classification of malware detection using malware detection. In Proceedings of the 2018 ACM SIGKDD international conference on computer science and technology, pp. 1-10. Cited by: SS1. * (3)[PERSON]. [PERSON] (1978)The automated classification of malware detection using malware detection. In Proceedings of the 2018 ACM SIGKDD international conference on computer science and technology, pp. 1-10. Cited by: SS1. * (4)[PERSON]. [PERSON] (1978)The automated classification of malware detection using malware detection. In Proceedings of the 2018 ACM SIGKDD international conference on computer science and technology, pp. 1-10. Cited by: SS1. * (5)[PERSON] (1978)The automated classification of malware detection using malware detection. In Proceedings of the 2018 ACM SIGKDD international conference on computer science and technology, pp. 1-10. Cited by: SS1.