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  • 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

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  • 原文地址:https://www.cnblogs.com/bonelee/p/9894361.html
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