## 3 Methodology
### Feature Extraction
#### 3.4.1 Texture Feature (SFTA)
The results are generated into three groups: (a) selection of features using traditional method (SFTA), (b) selection of features using traditional approach (SFTA) and pre-trained network (AlexNet, Inception-v3), and (c) deep CNN and SFTA feature fusion lengthwise with PCA selection method. All these experiments are employed on the malware dataset by applying PCA and the planned features collection method. We claim that we can build a high level of accuracy in recognizing malware from trusted samples deep learning with features derived from the pre-trained network and handcrafted method. These features are continuous and allow us to be more flexible with the classification. We use these continuous data and develop a novel classification method using a pre-trained network and handcrafted method to reduce over-fitting during training. We compare our method to a set of machine classification methods that have been applied in previous research and demonstrate an increase of classification accuracy using our method and an unseen dataset over the range of other machine classification methods that have been applied in previous research. Four experiments are performed using the Malimg balanced and imbalanced dataset to check the effectiveness of results, and the results are compared with the existing methods. 3.1 Experiment 1: With Imbalanced Data and SFTA Features without Feature Fusion and Feature Optimization

Experiment 1 is performed using the features obtained by applying SFTA on the Malimg dataset [51] and using the state-of-the-art classifiers. The imbalanced dataset has large different sized images in 25 classes. This shows the problem of over-fitting and has a negative influence on the results. The results based on imbalanced data and without feature fusion and feature optimization using SFTA are depicted in Table 3. The best accuracy is achieved using the quadratic SVM. The quadratic SVM on an imbalanced dataset obtained a maximum classification accuracy of 95.2%, recall of 69.0%, AUC of 90.0%, and ER of 4.8% on 10-fold cross-validation. Quadratic SVM obtained minimum ER and maximum AUC as compared to other classifiers such as weighted KNN, linear discriminate and some more in Table 3. The recall, ER, accuracy and AUC values of certain classifiers are also plotted in the graph shown in \[x_{0}=\frac{1}{N}\sum\
olimits_{i=0}^{N}x_{i}, \tag{20}\]0

#### 4.3.2 Experiment 2: With Balanced (Augmented) Data and SFTA Features

Experiment 2 was performed on balanced (augmented) data, using the SFTA features but without any feature fusion and optimization. The classification results are depicted in \[x_{0}=\frac{1}{N}\sum\
olimits_{i=0}^{N}x_{i}, \tag{20}\]1 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Recall (\%)** & **Accuracy (\%)** & **AUC (\%)** & **ER (\%)** \\ \hline Cubic Support Vector Machine (SVM) & 94.0 & 92.1 & 90.0 & 7.9 \\ Decision Tree & 36.0 & 72.4 & 81.0 & 27.6 \\ Weighted k-Nearest Neighbor (KNN) & 86.0 & 87.2 & 75.0 & 12.8 \\ Fine Gaussian & 49.0 & 81.4 & **93.1** & 18.6 \\ Fine KNN & 95.0 & 93.7 & 92.0 & 6.3 \\ Boosted Decision Tree & 85.9 & 85.2 & 92.0 & 14.5 \\ Adaboost & 91.0 & 90.2 & 89.0 & 9.8 \\ Linear Discriminate & 76.0 & 74.4 & 78.0 & 25.6 \\ Quadratic SVM & **96.0** & **95.2** & 90.0 & **4.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results using imbalanced dataset with SFTA features. Best values are shown in bold. Figure 7: Classification results (recall, accuracy, AUC and ER) using SFTA features on the original (imbalanced) dataset. The linear discriminant (LD) classifier used on a balanced dataset obtained a maximum classification accuracy of 99.3%, recall of 97.60%, AUC of 60.0%, and ER of 0.7% on 10-fold cross-validation. In the balanced data, accuracy is improved, and ER is smaller as compared to the imbalanced dataset. The image augmentation to obtain the balanced dataset has a positive influence on the results and solves the problem of overfitting, so accuracy is improved.