关键词: 恶意网页识别, 恶意网页特征, 机器学习, 生成对抗网络, 多判别器

Abstract:

Machine learning algorithm often needs to use a large amount of annotation data to train a classifier. However, it’s hard to collect malicious Web samples because of its short survival time. To solve this problem, a method based on Generative Adversarial Network（GAN） is proposed. The scheme uses a small amount of Web page sample set to train a generative adversarial network and generate Web page samples through its generator. Besides, several discriminators are added to the classical GAN structure to improve the quality of the generated samples. The global discriminator aims to improve the quality of the whole generated sample, and each feature discriminator makes the generated sample become detailed. As shown in the tests, the samples generated by the proposed scheme can be used to train the malicious Web page classifier. And its quality is better than the quality of the samples generated by the condition generative adversarial network and the conditional variational autoencoder.

Key words: malicious Web page detection, malicious Web page feature, machine learning, Generative Adversarial Network（GAN）, multiple discriminator