基于LSTM网络入侵检测的代码
时间: 2023-07-12 19:45:37 浏览: 71
以下是一个基于LSTM网络的入侵检测的代码示例:
```python
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.preprocessing import MinMaxScaler
# load data
df_train = pd.read_csv('train_data.csv')
df_test = pd.read_csv('test_data.csv')
# data preprocessing
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_train = scaler.fit_transform(df_train)
scaled_test = scaler.transform(df_test)
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), :]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 3
trainX, trainY = create_dataset(scaled_train, look_back)
testX, testY = create_dataset(scaled_test, look_back)
# create model
model = Sequential()
model.add(LSTM(64, input_shape=(look_back, 1), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(32))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# train model
model.fit(trainX, trainY, epochs=100, batch_size=32, verbose=2)
# evaluate model
scores = model.evaluate(testX, testY, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
```
在这个示例中,我们首先加载了训练和测试数据,然后使用MinMaxScaler将数据缩放到0和1之间。接着,我们定义了一个函数create_dataset,该函数将数据转换为LSTM模型所需的形式。然后,我们定义了一个LSTM模型,包括两个LSTM层和一个密集层。最后,我们使用fit函数来训练模型,并使用evaluate函数来评估模型的准确性。