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import torch
from torch import nn
import torch.nn.functional as F
malware_classes = ['7ev3n', 'APosT', 'Adposhel', 'Agent', 'Agentb', 'Allaple', 'Alueron.gen!J', 'Amonetize',
'Androm', 'Bashlite', 'Bingoml', 'Blacksoul', 'BrowseFox', 'C2LOP.gen!g', 'Convagent', 'Copak',
'Delf', 'Dialplatform.B', 'Dinwod', 'Elex', 'Emotet', 'Escelar', 'Expiro', 'Fakerean', 'Fareit',
'Fasong', 'GandCrab', 'GlobelImposter', 'GootLoader', 'HLLP', 'HackKMS', 'Hlux', 'IcedId', 'Infy',
'Inject', 'Injector', 'InstallCore', 'KRBanker', 'Koadic', 'Kryptik', 'Kwampirs', 'Lamer',
'LemonDuck', 'Loki', 'Lolyda.AA1', 'Lolyda.AA2', 'Mimail', 'MultiPlug', 'Mydoom', 'Neoreklami',
'Neshta', 'NetWireRAT', 'Ngrbot', 'OnlinerSpambot', 'Orcus', 'Padodor', 'Plite', 'PolyRansom',
'QakBot', 'QtBot', 'Qukart', 'REvil', 'Ramdo', 'Regrun', 'Rekt Loader', 'Sakula', 'Salgorea',
'Scar', 'SelfDel', 'Small', 'Snarasite', 'Stantinko', 'Trickpak', 'Upantix', 'Upatre', 'VB',
'VBA', 'VBKrypt', 'VBNA', 'Vilsel', 'Vobfus', 'WBNA', 'Wecod', 'XTunnel', 'Zenpak', 'Zeus', 'benign']
class DenseLayer(nn.Module):
def __init__(self, in_channels, growth_rate, bn_size):
super(DenseLayer, self).__init__()
self.bn1 = nn.BatchNorm2d(in_channels)
self.conv1 = nn.Conv2d(in_channels, bn_size * growth_rate, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(bn_size * growth_rate)
self.conv2 = nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)
def forward(self, x):
out = self.conv1(F.relu(self.bn1(x)))
out = self.conv2(F.relu(self.bn2(out)))
return torch.cat([x, out], 1)
class DenseBlock(nn.Module):
def __init__(self, num_layers, in_channels, growth_rate, bn_size):
super(DenseBlock, self).__init__()
layers = []
for i in range(num_layers):
layers.append(DenseLayer(in_channels + i * growth_rate, growth_rate, bn_size))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class TransitionLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(TransitionLayer, self).__init__()
self.bn = nn.BatchNorm2d(in_channels)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x):
out = self.conv(F.relu(self.bn(x)))
return self.pool(out)
class MalwareNet(nn.Module):
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, compression_rate=0.5, num_classes=87):
super(MalwareNet, self).__init__()
# First convolution
self.features = nn.Sequential(
nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(num_init_features),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
# Dense blocks
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = DenseBlock(num_layers, num_features, growth_rate, bn_size)
self.features.add_module(f'denseblock{i+1}', block)
num_features += num_layers * growth_rate
if i != len(block_config) - 1:
transition = TransitionLayer(num_features, int(num_features * compression_rate))
self.features.add_module(f'transition{i+1}', transition)
num_features = int(num_features * compression_rate)
# Final batch norm
self.features.add_module('norm5', nn.BatchNorm2d(num_features))
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
def forward(self, x):
features = self.features(x)
out = F.relu(features)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.classifier(out)
return out |