Network security risk event prediction method and device

Abstract

According to a network security risk event prediction method and device provided by the embodiment of the invention, the risk event category of the to-be-predicted network data is obtained through the twin neural network classification model, so that the problem that the network security risk event cannot be predicted when the network data sample size is too small or the network data sample distribution is unbalanced, and the accuracy of predicting the network security risk event is not high can be solved. In view of the problem of less marked information or unbalanced network data sample distribution in an actual application scene, the network data sample distribution imbalance degree is reduced by the simplest means, and for a small data set, the number of samples is greatly increased, and the possibility is provided for subsequently performing risk prediction by using a deep learning algorithm with higher fitting capability. When the network data sample size is sufficient and the network data sample distribution is balanced, the twin neural network classification model in the embodiment of the invention can achieve the best performance, and has the best AUC, GM and F1 performance.

Description

technical field [0001] The present invention relates to the technical field of network security, in particular to a network security risk event prediction method and device. Background technique [0002] With the increasing popularity of computers and the rapid development of network technology, the network is playing an increasingly important role in various fields such as society, politics, economy, and military affairs. However, more network security problems have emerged with the rapid development of the network. , and risk prediction technology can effectively predict attacks or hazards, and plays an important role in network security. It can accurately locate and predict various types of network hazards, laying the foundation for formulating countermeasures. Cybersecurity risks come in all forms, such as spear-phishing emails, malware, and malicious web pages. [0003] How to design an efficient network security risk event prediction method has attracted widespread a...

AI Technical Summary

Problems solved by technology

For example, when undersampling reduces non-risk samples, it is easy to ignore potentially useful data; while oversampling is prone to overfitting problems when there are fewer risk samples; threshold movement needs to be dynamically adjusted according to the input data and the output characteristics of the model

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