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首页> 外文会议>IEEE International Conference on Software Engineering and Service Science >Network Security Situation Prediction Model Based on Multi-Swarm Chaotic Particle Optimization and Optimized Grey Neural Network
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Network Security Situation Prediction Model Based on Multi-Swarm Chaotic Particle Optimization and Optimized Grey Neural Network

机译：基于多群混沌粒子优化和优化灰色神经网络的网络安全态势预测模型

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摘要

Network situation value is an important index to measure network security. Establishing an effective network situation prediction model can prevent the occurrence of network security incidents, and plays an important role in network security protection. Through the understanding and analysis of the network security situation, we can see that there are many factors affecting the network security situation, and the relationship between these factors is complex., it is difficult to establish more accurate mathematical expressions to describe the network situation. Therefore, this paper uses the grey neural network as the prediction model, but because the convergence speed of the grey neural network is very fast, the network is easy to fall into local optimum, and the parameters can not be further modified, so the Multi-Swarm Chaotic Particle Optimization (MSCPO)is used to optimize the key parameters of the grey neural network. By establishing the nonlinear mapping relationship between the influencing factors and the network security situation, the network situation can be predicted and protected.
机译：网络状况值是衡量网络安全性的重要指标。建立有效的网络态势预测模型可以防止网络安全事件的发生，在网络安全保护中起着重要的作用。通过对网络安全状况的理解和分析，可以发现影响网络安全状况的因素很多，而且这些因素之间的关系十分复杂，难以建立更准确的数学表达式来描述网络状况。因此，本文采用灰色神经网络作为预测模型，但由于灰色神经网络的收敛速度非常快，因此该网络容易陷入局部最优，且参数无法进一步修改，因此Multi -群混沌粒子优化（MSCPO）用于优化灰色神经网络的关键参数。通过建立影响因素与网络安全状况之间的非线性映射关系，可以预测和保护网络状况。

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