## 4 The Proposed Methods
9: An array has been generated from the image. 10: Reducing the features array's dimensions to one. 11: Print the flattened array. 12: Keeping the generated 1000 feature vector. End
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**Algorithm 3********DenseNet201 model as feature extraction

Because it employs inception modules that minimize the number of parameters that must be learned, GoogLeNet is renowned for its parameter economy. Here, features were extracted from the final \"FC1000\" layer of the GoogLeNet model. The basic structure of GoogLeNet for feature extraction is displayed in Algorithm 4. The GoogLeNet feature vector that is produced is 1 \(\times\) 5643. Step 2.4: AlexNet Features Extraction

There are fifteen layers in the AlexNet design. In order to minimize spatial dimensions, the network starts with convolutional layers and then moves on to max-pooling layers. The classification layer is then eliminated, and the final product of the convolutional layers is flattened. The basic structure of AlexNet for feature extraction is displayed in Algorithm 5. The AlexNet feature vector that is produced is 1 \(\times\) 4096 from the last two fully connected layers (FCLs). ```
Input: RGB Malware Image from dataset. Output: 1 \(\times\) 4096 features vector dimension. Begin For  1: Upload the preparation malware image. 2: Normalize each image input that falls between the 0 and 255. 3: Change the resolution to (224 \(\times\) 244). 4: Load the GoogLeNet model. 5: Load AlexNet model weights. 6: The model's last fully connected (FC) layers have been eliminated or are not included. 7: Build an entirely new model for feature extraction. 8: Using GoogLeNet final convolutional layer (\"pool5-drop_7\(\times\)7_s1\"), the activation is produced. 9: An array has been generated from the image. 10: Reducing the features array's dimensions to one. 11: Print the flattened array. 12: Keeping the generated 5643 feature vector. End for End
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**Algorithm 5** AlexNet model as feature extraction. Step 2.5: SqueezeNet Features Extraction

The fifteen layers that make up SqueezeNet are divided into five distinct levels: two layers for convolution, three layers for pooling, eight layers for fire, one global average layer for pooling, and an ultimate classification layer that will be eliminated. Because it has a smaller number of parameters, the network becomes more efficient. The basic structure of SqueezeNet for feature extraction is displayed in Algorithm 6. The SqueezeNet feature vector that is produced is 1 \(\times\) 1000 from the last fully connected layers (pool10). ```
Input: RGB Malware Image from dataset. Output: 1 \(\times\) 1000 features vector dimension. Begin For 1: Upload the preparation malware image. 2: Normalize each image input that falls between the 0 and 255. 3: Change the resolution to (227 \(\times\) 247). 4: Load the SqueezeNet model. 5: Load SqueezeNet model weights. 6: The model's last fully connected (FC) layers have been eliminated or are not included. 7: Build an entirely new model for feature extraction. 8: Using SqueezeNet final convolutional layer (\"avg_pool10\"), the activation is produced. 9: An array has been generated from the image. 10: Reducing the features array's dimensions to one. 11: Print the flattened array. 12: Keeping the generated 1000 feature vector. End for End
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**Algorithm 6** SqueezeNet model as feature extraction. Step 3: Feature Selection

By keeping useful features and eliminating duplicate and unnecessary ones, feature selection is essential to lowering the overall dimension of the dataset. Therefore, principal component analysis (PCA) is used because it is a well-known and useful approach for feature selection. PCA feature selection is a technique for reducing a dataset's total amount of parameters while keeping the most essential information. Selecting only the most significant components is the primary step in PCA, which involves transforming the original dataset into a new collection of variables that are linearly combined with the original variables [37]. PCA works in four different steps.