基于神经网络的网络安全态势感知

随着国家经济的发展,科学技术也在不断的提升,在此基础上形成的互联网络结构也得到了相应的发展,但是伴随着社会的复杂程度的加深,其网络安全也存在较多问题,因此,针对目前网络安全态势感知不足的问题,利用多种方式对其进行比较分析,借助网络安全态势值的非线性时间序列特点,以神经网络为核心点,处理相关的混沌与非线性数据,并以此为理论基础提出RBF神经网络,进行网络安全的态势预测。此种方法是利用RBF神经网络来明晰神经网络,并找到其中的前N个数据,以及随后的M个数据,借助专业的方法进行非线性映射关系计算,利用这种关系可以很好的进行神经网络安全态势值的预测。

Network security situation awareness based on Neural Network

with the development of national economy,science and technology are also constantly upgrading,network structure on the basis of the formation has also been a corresponding development,but with the deepening of the complexity of society,the network security also exist many problems,therefore,in view of the current network security situation awareness of the problem of insufficient use of a variety of ways carries on the comparative analysis,by using the nonlinear characteristics of time series of network security situation value,uses the neural network as the core point,treatment of chaos and nonlinear data,and on the basis of the theory put forward RBF neural network,prediction of network security situation.This kind of method is to clarify the neural network using RBF neural network,and find the first N data and M data subsequently,calculate the nonlinear mapping relationship with professional methods,the use of this relationship can predict neural network security situation is very good.