from keras.models import Sequential from keras.layers import Conv1D, MaxPooling1D, Flatten, LSTM, Dense, BatchNormalization, Activation, Reshape model = Sequential() model.add(Conv1D(numFilters, filterSize, padding='same', input_shape=inputSize)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling1D(pool_size=2, strides=2)) model.add(Conv1D(numFilters, filterSize, padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling1D(pool_size=4, strides=2)) model.add(Conv1D(2numFilters, filterSize, padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling1D(pool_size=4, strides=2)) model.add(Conv1D(2numFilters, filterSize, padding='same')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling1D(pool_size=4, strides=2)) model.add(Flatten()) model.add(Reshape((1, -1))) model.add(LSTM(numHiddenUnits, return_sequences=False)) model.add(Dense(numClasses, activation='softmax'))改写成适合处理一维异常流量的代码

import tensorflow as tf
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, LSTM, Dense, BatchNormalization, Activation, Reshape

model = tf.keras.Sequential([
Conv1D(filters=numFilters, kernel_size=filterSize, padding='same', input_shape=inputSize),
BatchNormalization(),
Activation('relu'),
MaxPooling1D(pool_size=2, strides=2),
Conv1D(filters=numFilters, kernel_size=filterSize, padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling1D(pool_size=4, strides=2),
Conv1D(filters=2*numFilters, kernel_size=filterSize, padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling1D(pool_size=4, strides=2),
Conv1D(filters=2*numFilters, kernel_size=filterSize, padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling1D(pool_size=4, strides=2),
LSTM(numHiddenUnits, return_sequences=False),
Dense(numClasses, activation='softmax')
])

# 对于异常流量，可以使用异常检测模型，如Autoencoder等，将其与该模型结合起来使用。