Differential WGAN based network security situation prediction method

Abstract

The invention provides a differential WGAN based network security situation prediction method. The GAN (Generative adversarial network) is used to simulate the development process of situation, and the situation is predicted in the time dimension. A loss function with the Wasserstein distance as the GAN is used to solve the problem that the GAN is hard to train and instable in collapse mode and gradient, and a differential item is added to the loss function to improve the classification precision of a situation value. The stability of the differential WGAN network is proved. According to experimental results and analysis, the mechanism has advantages in the aspects in convergence, prediction precision and complexity compared with other mechanisms.

Description

technical field [0001] The invention relates to the technical field of network security situation prediction machine learning, in particular to network security situation prediction based on differential WGAN. Background technique [0002] Nowadays, the global cyberspace is facing huge security challenges, such as frequent national hacking incidents, continuous attacks on critical infrastructure and the Internet of Things, the prevalence of ransomware, serious data leakage, etc. How the future network security will develop and how to accurately predict the network security situation will be the focus of future research. Network security situation prediction is the ultimate goal of Network Security Situation Awareness (NSSA). [0003] In the field of network security, situation prediction has become a hot topic. Network security situation prediction is to analyze and predict the possible development trend of the network situation in the future by using expert knowledge and ...

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