## 4 The Proposed Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Properties** \\ \hline PC & HP laptop (Hewlett-Packard Company, Palo Alto, CA, USA) \\ \hline \multirow{2}{*}{Operating system} & Microsoft Windows 10 64-bit (OS) \\  & (Microsoft, Redmond, DC, USA) \\ \hline RAM & 8 GB \\ \hline \multirow{2}{*}{Processor} & Intel(R) Core(TM) i7-6500U CPU @ 2.50 GHz 2.60 GHz \\  & (Intel, Santa Clara, CA, USA) \\ \hline \multirow{2}{*}{Software} & MATLAB version R2020a \\ \cline{2-3}  & Intel® HD Graphics 520 \\ \cline{2-3}  & (NVIDIA GTX 950M) (NVIDIA, Santa Clara, CA, USA) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimentation specifications description. ### Performance Evaluation Metric

Two metrics, namely detection accuracy and error, are employed to assess the proposed technique. The following formula [13] can be used to determine these measures:

1. Accuracy: the ratio of successfully classified photos to the overall number of input images, in accordance with Equation (1). \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (1)
2. Error: the percentage of all images to all images that were wrongly classified, in accordance with Equation (2). \[Error=\frac{FP+FN}{TP+TN+FP+FN}\] (2)

Figure 11 lists each term that may be ascertained using the previously provided equations. ### Evaluation Results

In order to guarantee the precision and reliability of the experimental results, a 10-fold cross-validation technique is employed [48]. By merging multiple different datasets, this approach minimizes bias in performance evaluation [49]. One part is used for the test set, while the other nine are selected for the training set. There are ten tests conducted in all, and the procedure is finally assessed by adding up the results of each experiment. To find the most effective classifier among a variety of pretrained model features, accuracy, and error measurements are computed. The accuracy and error results of five pretrained models feature extraction techniques and six classification techniques across the Malimg databases are displayed. To further verify the efficacy of findings, tests are carried out with the Malimg unbalanced datasets. #### 5.3.1 Performance Findings Using an Unbalanced Malimg Dataset

We evaluate the performance measures of the full extracted features (FEFs) and selected extracted features (SEFs) on the Malimg dataset. The FEFs are obtained from five pretrained models, including ResNet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet. Using the PCA method, different feature dimensions, such as 50 and 500, are used to obtain the SEFs. Finally, the GDA, KNN, LOG, SVM, RF, and ELM classifiers are used to perform the classification method for binary classification (malware versus non-malware (benign)). Accuracy and error are among the metrics taken into consideration for the evaluation. These metrics offer an extensive assessment of the models' performance. The performance metrics for the FEFs and SEFs of ResNet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet features are summarized in Tables 5-9, respectively. A selection of assessment criteria, such as accuracy and error, is made in order to compare all classifiers. Evidently, the bold stands for the highest values. Figure 11: A detailed description of every term.