Self-corrected coefficient smoothing method based network security situation prediction

Hongyu YANG; Xugao ZHANG

doi:10.11959/j.issn.1000-436x.2020092

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Self-corrected coefficient smoothing method based network security situation prediction

Correspondences | 更新时间：2024-06-05

- Self-corrected coefficient smoothing method based network security situation prediction
- Journal on Communications Vol. 41, Issue 5, Pages: 196-204(2020)
- 作者机构：
  
  中国民航大学计算机科学与技术学院，天津 300300
- 作者简介：
- 基金信息：
  
  The Civil Aviation Joint Research Fund Project of National Natural Science Foundation of China(U1833107)
- DOI：10.11959/j.issn.1000-436x.2020092
  CLC： TP309
- Online First：2020-05，
  
  Published：25 May 2020
- 稿件说明：
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Hongyu YANG, Xugao ZHANG. Self-corrected coefficient smoothing method based network security situation prediction[J]. Journal on Communications, 2020, 41(5): 196-204.
DOI：

Hongyu YANG, Xugao ZHANG. Self-corrected coefficient smoothing method based network security situation prediction[J]. Journal on Communications, 2020, 41(5): 196-204. DOI： 10.11959/j.issn.1000-436x.2020092.

摘要

针对目前网络安全态势预测方法的精确度不足问题，以自修正系数修匀法为基础提出一种新的网络安全态势预测模型。首先，设计一种网络安全态势评估量化方法，基于熵关联度将警报信息转化为态势实际值时间样本序列。然后，计算静态修匀系数自适应解并利用可变域空间获取预测初始值。最后，为了进一步提高预测精度，基于偏差类别并采用时变加权马尔可夫链对网络安全态势初始预测结果进行修正。采用LL_DOS_1.0数据集检验预测效果，实验结果表明，所提模型面向网络态势时间序列具有较高的自适应性和预测精度。

Abstract

In order to solve the problem of insufficient accuracy of current network security situation prediction methods

a new network security situation prediction model was proposed based on self-correcting coefficient smoothing.Firstly

a network security assessment quantification method was designed to transform the alarm information into situation real value time series based on the entropy correlation degree.Then

the adaptive solution of the static smoothing coefficient was calculated and the predicted initial value was obtained by using the variable domain space.Finally

based on the error category

the time-changing weighted Markov chain was built to modify the initial network situation prediction result and the prediction accuracy was further raised.The prediction model was tested with LL_DOS_1.0 dataset and the experimental results show that the proposed model has higher adaptability and prediction accuracy for network situation time series.

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references

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