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dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 dce6da2 461e792 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | """
Contains the CNN model for malware classification.
"""
import torch.nn as nn
class MalwareCNN(nn.Module):
"""
Convolutional Neural Network for classifying malware families based on byte images.
Args:
num_classes (int): Number of unique malware families to classify.
"""
def __init__(self, num_classes=24):
super(MalwareCNN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # 128 -> 64
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # 64 -> 32
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # 32 -> 16
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(128 * 16 * 16, 512),
nn.ReLU(),
nn.Dropout(0.6), # Increased dropout to prevent overfitting
nn.Linear(512, num_classes),
)
def forward(self, x):
"""
Forward pass through the CNN.
Args:
x (torch.Tensor): Input batch of images.
Returns:
torch.Tensor: Logits for each class.
"""
x = self.features(x)
x = self.classifier(x)
return x
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