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1D-CNN model for Intrusion Detection.
Applies 1D convolutions over the feature vector.
"""
import torch
import torch.nn as nn
class CNN1D_IDS(nn.Module):
"""
1D Convolutional Neural Network for IDS.
Design choices:
- Reshape (batch, 41) → (batch, 1, 41): treat features as 1-channel signal
- Two Conv1d layers (64, 128 filters): learn local feature patterns
- Kernel size 3: captures triplets of adjacent features
- AdaptiveAvgPool1d(8): fixed output size regardless of input length
"""
def __init__(self, in_dim=41, num_classes=2):
super().__init__()
self.conv = nn.Sequential(
nn.Conv1d(1, 64, kernel_size=3, padding=1),
nn.BatchNorm1d(64),
nn.ReLU(),
nn.Conv1d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.AdaptiveAvgPool1d(8)
)
self.fc = nn.Sequential(
nn.Linear(128 * 8, 64),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(64, num_classes)
)
def forward(self, x):
# x: (batch, 41) → (batch, 1, 41)
x = x.unsqueeze(1)
x = self.conv(x) # (batch, 128, 8)
x = x.view(x.size(0), -1) # (batch, 1024)
return self.fc(x)
def count_parameters(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
if __name__ == '__main__':
model = CNN1D_IDS(in_dim=41, num_classes=2)
print(model)
print(f"\nTotal parameters: {model.count_parameters():,}")
x = torch.randn(32, 41)
out = model(x)
print(f"Input: {x.shape}")
print(f"Output: {out.shape}")
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