## 2 Related Works  
Here, feature engineering is not necessary for the following methods. We employed image unprocessed pixel information as our foundational malware representation. Furthermore, to improve the classification and detection outcomes, we used knowledge transfer from a deep neural network trained for object detection tasks on a different dataset to find good malware descriptions. Numerous academics have looked into the use of machine learning techniques and deep learning models for malware detection. Convolutional neural networks (CNNs) are one of the most widely used deep learning techniques because they make feature extraction simpler to perform. Researchers used deep learning as a method for feature extraction for identifying malware in order to improve the classifier's performance. CNN structures that were previously trained before building the classification model can be used to extract the features. The role of the pretrained CNN model is to recognize and categorize different types of malware.  
In this section, a full study of several of the most important malware detection works relying on pretrained CNN model features was analyzed. [PERSON] et al. [6] proposed a malware detection method based on a pretrained VGG16 model. Using the ImageNet dataset as training data, they first retrieved features from the bottleneck layer of VGG16. During the classification stage, the SVM classifier was employed. [PERSON] et al. [7] proposed a malware detection method based on a pretrained ResNet50 model. A PCA procedure was utilized in order to decrease the dimension of the feature vector. Several classification techniques, including CNN architecture ensemble, SoftMax, and multiclass SVMs, were employed throughout the classification step. [PERSON] et al. [8] proposed a malware detection method based on a pretrained VGG16 model. The proposed method was tested using the Malimg Database. Customized CNN outperformed VGG16, ResNet-18, and InceptionV3 in terms of accuracy because of its capacity to process small and non-uniform input for the classification of malware. [PERSON] et al. [9] Proposed malware detection based on pretrained CNN models that had already been trained on ImageNet for classification. An evaluation of the proposed method was carried out using the Malimg and BIG2015 databases. Compared to pretrained models like VGG16, VGG19, ResNet50, and InceptionV3, the customized CNN model performed better. [PERSON] et al. [10] presented a comparative analysis of several pretrained models, including the VGG16, VGG19, DenseNet, and AlexNet models. Principal component analysis (PCA) is used to apply feature extraction. Next was the Particle Swarm Optimization (PSO) feature selection process. The proposed method was tested using the Malimg database. A CNN was used in the classification stage. [PERSON] et al. [11] presented a malware detection method relying on ResNet-18 and DenseNet201 models. The second layer of the classification layer's deep features were put together to create a boosted feature set, which was then given to SVM. [PERSON] et al. [12] presented a malware detection method relying on merging two pretrained models, such as ResNet and Alex Net. The proposed method was tested using the Malimg database. Despite achieving a high accuracy percentage, the generated vector had large dimensions (4096). [PERSON] et al. [13] proposed a malware detection method based on the GoogLeNet model. Several classification algorithms, such as KNN, SVM, and ELM, were employed during the classification stage. The proposed method was examined on common unbalanced Malimg datasets. The accuracy percentage of the presented method exceeded the previous methods, namely that of the deep-feature and hand-crafted feature methods. Lastly, Table 1 provides a summary of the current appropriate works in the malware detection field.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline