用pytorch搭建基于cnn的网络安全入侵检测模型代码

我可以回答这个问题。以下是一个使用 PyTorch 搭建基于 CNN 的网络安全入侵检测模型的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 7 * 7)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Load data
train_data = np.load('train_data.npy')
train_labels = np.load('train_labels.npy')
test_data = np.load('test_data.npy')
test_labels = np.load('test_labels.npy')

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i in range(len(train_data)):
        inputs = torch.from_numpy(train_data[i]).unsqueeze(0).unsqueeze(0).float()
        labels = torch.tensor([train_labels[i]], dtype=torch.long)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_data)))

# Test the model
correct = 0
total = 0
with torch.no_grad():
    for i in range(len(test_data)):
        inputs = torch.from_numpy(test_data[i]).unsqueeze(0).unsqueeze(0).float()
        labels = torch.tensor([test_labels[i]], dtype=torch.long)

        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)

        total += 1
        correct += (predicted == labels).sum().item()

print('Accuracy: %.2f%%' % (100 * correct / total))

这个模型使用了两个卷积层和两个全连接层，可以用来对网络流量进行分类，判断是否存在入侵行为。