深度学习在网络管理中的应用：智能化的新时代

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 加载数据
data = pd.read_csv('network_traffic.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

# 创建训练和测试数据集
train_size = int(len(scaled_data) * 0.8)
train_data = scaled_data[:train_size]
test_data = scaled_data[train_size:]

# 创建数据集函数
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back):
        a = dataset[i:(i + look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 10
X_train, y_train = create_dataset(train_data, look_back)
X_test, y_test = create_dataset(test_data, look_back)

# 重塑输入数据
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# 构建LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(look_back, 1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')

# 训练模型
model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))

# 预测
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 反向缩放
train_predict = scaler.inverse_transform(train_predict)
test_predict = scaler.inverse_transform(test_predict)

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# 加载数据
data = pd.read_csv('network_anomalies.csv')
X = data.drop('Label', axis=1)
y = data['Label']

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
X_scaled = scaler.fit_transform(X)

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# 构建深度学习模型
model = Sequential()
model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))

# 评估模型
loss, accuracy = model.evaluate(X_test, y_test)
print(f'测试集准确率: {accuracy:.2f}')

from tensorflow.keras.utils import to_categorical

# 加载数据
data = pd.read_csv('network_traffic_classification.csv')
X = data.drop('Class', axis=1)
y = data['Class']

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
X_scaled = scaler.fit_transform(X)
y_encoded = to_categorical(y)

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_encoded, test_size=0.2, random_state=42)

# 构建深度学习模型
model = Sequential()
model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(y_encoded.shape[1], activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))

# 评估模型
loss, accuracy = model.evaluate(X_test, y_test)
print(f'测试集准确率: {accuracy:.2f}')