基于G-K算法的网络安全态势预测模型

发布时间：2018-07-05 13:53

  本文选题：G-K算法 + 网络安全　； 参考：《科技通报》2017年11期

【摘要】：针对普通Kalman算法在网络安全态势预测中对初始数据的依赖性较高,且预测精度不够高的问题,本文提出了一种基于G-K算法的网络安全态势预测模型。首先利用灰关联熵分析方法选出影响网络安全态势的关键因素,然后结合关键因素建立网络安全态势的多元关系模型,最后选用KDD-cup99的部分数据作为实验数据源对改进算法进行实例仿真。结果表明,G-K算法能够快速跟踪网络安全态势的变化趋势,预测精度优于普通Kalman算法。
[Abstract]:In order to solve the problem that the common Kalman algorithm has high dependence on the initial data in the network security situation prediction and the prediction accuracy is not high enough, this paper proposes a network security situation prediction model based on the G-K algorithm. First, the key factors that affect the network security situation are selected by the grey relational entropy analysis method, and then the key factors are combined with the key factors. The multi relation model of network security situation is established. Finally, some data of KDD-cup99 are selected as the experimental data source to simulate the improved algorithm. The results show that the G-K algorithm can quickly track the trend of network security situation, and the prediction accuracy is better than the common Kalman algorithm.
【作者单位】： 山西电力职业技术学院;青岛科技大学自动化学院;
【分类号】：TP393.08
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本文编号：2100423