关键词: 态势要素, 样本特征, 卷积神经网络, 迁移学习, 生成对抗网络

Abstract: For the traditional element acquisition mechanism, when the sample distribution is unbalanced, the samples with a small proportion（collectively referred to as small samples） cannot be effectively detected. Accurately identifying each attack sample has become one of the research hotspots, and an element acquisition model for imbalance sample is proposed by using deep learning. Firstly, it uses convolutional neural network as the base classifier to extract the deep features of network data. Then, GAN is used to generate a countermeasure network to expand small samples to solve the problem of unbalanced sample distribution. Finally, transfer learning is used on the expanded balanced data set to speed up the training time of the base classifier to the new classification adapted to small samples. Experiments on the NSL-KDD dataset show that the dataset expanded by generating the confrontation network, combined with transfer learning, effectively accelerates the model training convergence speed, and effectively improves the classification accuracy of the network security situation elements.

Key words: situation element, sample characteristics, convolutional neural network, transfer learning, generative adversarial network