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计算机网络安全评估的研究和安全评价指标体系,不仅仅是一个计算机网络的漏洞,如病毒入侵是一个复杂的非线性问题,传统的线性评价方法不能准确地描述评价结果的影响和低评价结果的准确性。为了提高评价计算机网络安全的精准度,本文提出了一个粒子优化神经网络的计算机网络安全评价方法。首先选择通过专家系统是指计算机网络安全评估,然后使用专家评分法确定评价指标权重,将指标权重输入BP神经网络学习,BP神经网络的参数是由粒子群优化的计算机网络安全评价水平组成。仿真结果表明,与传统的计算机网络安全评价模型相比,粒子优化神经网络加快计算机网络安全评价,提高计算机网络安全的评估精准度。
Computer network security assessment research and security evaluation index system, not just a computer network vulnerabilities, such as virus invasion is a complex nonlinear problem, the traditional linear evaluation method can not accurately describe the impact of evaluation results and low evaluation results accuracy. In order to improve the accuracy of computer network security evaluation, this paper presents a particle-optimized neural network computer network security evaluation method. Firstly, choosing the expert system means computer network security assessment, then using expert scoring method to determine the weight of evaluation index, and inputting the index weight into BP neural network learning. The parameters of BP neural network are composed of particle swarm optimization computer network security evaluation level. Simulation results show that, compared with the traditional computer network security evaluation model, particle-optimized neural network can speed up the evaluation of computer network security and improve the evaluation accuracy of computer network security.