使用GAN进行异常检测——可以进行网络流量的自学习哇，哥哥，人家是半监督，无监督的话，还是要VAE，SAE。

实验了效果，下面的还是图像的异常检测居多。

https://github.com/LeeDoYup/AnoGAN

https://github.com/tkwoo/anogan-keras

看了下，本质上是半监督学习，一开始是有分类模型的。代码如下，生产模型和判别模型：

### generator model define

def generator_model():

    inputs = Input((10,))

    fc1 = Dense(input_dim=10, units=128*7*7)(inputs)

    fc1 = BatchNormalization()(fc1)

    fc1 = LeakyReLU(0.2)(fc1)

    fc2 = Reshape((7, 7, 128), input_shape=(128*7*7,))(fc1)

    up1 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(fc2)

    conv1 = Conv2D(64, (3, 3), padding='same')(up1)

    conv1 = BatchNormalization()(conv1)

    conv1 = Activation('relu')(conv1)

    up2 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv1)

    conv2 = Conv2D(1, (5, 5), padding='same')(up2)

    outputs = Activation('tanh')(conv2)

    model = Model(inputs=[inputs], outputs=[outputs])

    return model

### discriminator model define

def discriminator_model():

    inputs = Input((28, 28, 1))

    conv1 = Conv2D(64, (5, 5), padding='same')(inputs)

    conv1 = LeakyReLU(0.2)(conv1)

    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, (5, 5), padding='same')(pool1)

    conv2 = LeakyReLU(0.2)(conv2)

    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    fc1 = Flatten()(pool2)

    fc1 = Dense(1)(fc1)

    outputs = Activation('sigmoid')(fc1)

    model = Model(inputs=[inputs], outputs=[outputs])

    return model

对于无监督GAN就搞不定了！

https://zhuanlan.zhihu.com/p/32505627

https://arxiv.org/pdf/1805.06725.pdf

https://www.ctolib.com/tkwoo-anogan-keras.html

https://github.com/trigrass2/wgan-gp-anomaly/tree/master/models