基于混沌粒子群优化BP神经网络的网络安全风险评估系统设计

网络规模不断扩大的同时,也容易受到各种安全风险的威胁,因此,必须对网络安全风险进行准确评估。传统的评估系统中存在的趋势性、周期性以及随机性影响评估准确率的问题,导致评估的结果大都不准确;为此,提出并设计了基于混沌粒子群优化BP神经网络的网络安全风险评估系统。首先对系统的硬件进行了设计,并得出了设计的框图;然后使用混沌粒子群的优化算法和BP神经网络的算法对系统的软件进行了设计;最后进行了对比的实验。实验结果表明,该系统能够更好的协调,并处理评估过程中出现的问题,不会受到趋势性、周期性以及随机性的影响,能够更好的发挥网络安全评估的效果,提高评估的准确率,减小相对的误差。

Design of Network Security risk Assessment system based on chaotic Particle Swarm Optimization BP Neural Network

As the scale of network expands, it is easy to be threatened by all kinds of security risks. Therefore, it is necessary to evaluate the network security risks accurately. The trend, periodicity and randomness of the traditional evaluation system affect the accuracy of the assessment, which results in inaccurate results. A network security risk assessment system based on chaotic particle swarm optimization (BP) neural network was proposed and designed. Firstly, the hardware of the system was designed, and the block diagram was obtained. Then, the software of the system was designed by using the algorithm of chaos particle swarm optimization and the algorithm of BP neural network. . The experimental results show that the system can better coordinate, and deal with the problems in the evaluation process, will not be affected by the trend, periodicity and randomness, and can play a better role in network security evaluation, improve the accuracy of evaluation and reduce the relative error.