## 4 The Proposed Methods  
The process used for comparison and the proposed method focuses on the four primary stages of malware detection (malware transformation, feature extraction, classification, and evaluation). Initially, we convert the malware RGB images into grayscale versions. The feature extraction process extracts the features for each grayscale image from various models such as ResNet50, GoogLeNet, AlexNet, DenseNet201, and SqueezeNet. The classification is carried out via GDA, KNN, logistic, SVM, RF, and ensemble learning. The application of assessment measures allows for the examination of a better classifier. A confusion matrix can be used to establish these measurement ratios. Figure 9 depicts a process diagram for the process for comparison and the presented method. In addition to that, Algorithm 1 was created as well. The subsections that follow contain all of the specifics for each stage. Figure 8: The fundamental design of SqueezeNet. Figure 9: A general diagram regarding the proposed method and comparison process. ```
Input: RGB Malware Image from dataset. Output: Non-Malware/Malware Image. Begin For Step 1: Load the dataset Step 2: Read all the images using the \"Imread (0\" function to read each image; Step 3: The RGB image should be converted to the grayscale image through the use of a Matlab code called \"rgb2 gray 0\"; Step 4: Resizing Process. Step 4.1: For Resnet50, DenseNet201, and GoogLeNet model, Resize the converted images into (224, 224) pixels. Step 4.2: For AlexNet and SqueezeNet model, Resize the converted images into (227, 227) pixels. Step 5: Load pre-trained CNN models (Resnet50, DenseNet201, GoogLeNet, AlexNet, and SqueezeNet). Step 6: Activations Process: Step 6.1: Outputs from (\" fc1000\") the last convolutional layer of ResNet50. Step 6.2: Outputs from (fc1000) the last layer of DenseNet201. Step 6.3: Outputs from (\"pool5-drop_7\(\times\)7_s1\") the last layer of GoogLeNet (Inception V3). Step 6.4: Outputs from (fc7) the last layer of AlexNet. Step 6.5: Outputs from (pool10) the last layer of SqueezeNet. Step 7: Extract Feature Vector. Step 7.1: For ResNet50, Store the 1000 feature vector dimension. Step 7.2: For DenseNet201, Store the 1000-feature vector dimension. Step 7.3: For GoogLeNet, Store the 1024-feature vector dimension. Step 7.4: For AlexNet, Store the 4096-feature vector dimension. Step 7.5: For SqueezeNet, Store the 1000 feature vector dimension. Step 8: Apply feature selection method: Step 8.1: Apply PCA method. Step 9: Replace the fully connection layers FC with new classifier. Step 9.1: Training Process: 1. Train the GDA classifier on the selected extracted feature vectors; 2. Train the KNN classifier on the selected extracted feature vectors; 3. Train the Logistics classifier on the selected extracted feature vectors; 4. Train the SVM classifier on the selected extracted feature vectors; 5. Train the RF classifier on the selected extracted feature vectors; 6. Train the Ensemble classifier on the selected extracted feature vectors; Step 9.2: Testing Process: 1. The GDA model that has been trained is put through a series of tests to determine if an image contains malware or not. 2. The KNN model that has been trained is put through a series of tests to determine if an image contains malware or not. 3. The Logistics model that has been trained is put through a series of tests to determine if an image contains malware or not. 4. The SVM model that has been trained is put through a series of tests to determine if an image contains malware or not. 5. The RF model that has been trained is put through a series of tests to determine if an image contains malware or not. 6. The Ensemble model that has been trained is put through a series of tests to determine if an image contains malware or not. End for [PERSON] et al. [35] first introduced the binary visualization technique to portray binary information elements as grayscale images. This means that white is represented by 255, black by 0, and other numbers are intermediary grayscale shades.