神经网络在计算机网络安全评价中的应用研究

摘要

研究计算机网络安全评价问题,计算机网络安全评价是一个多指标系统,计算机网络受到漏洞、病毒等人侵是一个复杂的非线性问题,传统线性评价方法不能准确描述各指标对评价结果影响且评价结果的精度低.为了提高计算机网络安全的评价精度,提出了一种粒子优化神经网络的计算机网络安全评价方法.首先通过专家系统挑选计算机网络安全评价指标,然后采用专家打分方法确定评价指标权重,最后将指标权重输入BP神经网络进行学习,BP神经网络参数通过粒子群算法进行优化,获得计算机网络安全评价等级.仿真结果表明,相对于传统计算机网络安全评价模型,粒子优化神经网络加快计算机网络安全评价速度,提高了计算机网络安全的评价精度.%Research computer network security problem. There are nonlinear relations among the evaluation indexes, and it is difficult for an accurate mathematical model to describe the nonlinear relationship. In order to improve the evaluation accuracy of computer network security, we puts forward a combination model to evaluate the computer network security. The combination model used particle swarm optimization (PSO) to optimize the parameters of BP neural network, speed up the BP neural network' s convergence speed, and enhance its global optimization ability, which effectively improved the accuracy of the evaluation model. Simulation results showed that compared with traditional BP neural network model, the combined model's learning ability is faster and global search ability is stronger, which effectively improves the evaluation accuracy of computer network security.