A Framework for Few-Shot Network Threats Based on Generative Adversarial Networks

A small amount of malicious logs is confusing and unbalanced for a large number of normal logs. We proposed a framework of network threats sample based on Generative Adversarial Networks(GAN). This paper solves the imbalance problem of multidimensional sample data such as logs, traffic, programs, and feature spaces in the field of cyberspace security by generating confrontation networks. We carried out a large-scale confrontation generation experiment of security event logs based on SeqGAN and generated corresponding log text for data enhancement, which effectively solve the problem of few-shot. The results in this section show that the use of the AC-GAN augmentation dataset is enhanced compared to the original non-equilibrium dataset using the artificial synthesis of the SMOTE dataset Network traffic data set to improve the performance of supervised learning classification. It has inestimable effects on threat detection, various types of offensive to defensive, and cryptography algorithms.