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Research on gray correlation analysis and situation prediction of network information security
EURASIP Journal on Information Security Pub Date : 2021-04-20 , DOI: 10.1186/s13635-021-00118-1
Chengqiong Ye , Wenyu Shi , Rui Zhang

In order to further improve the accuracy and efficiency of network information security situation prediction, this study used the dynamic equal-dimensional method based on gray correlation analysis to improve the GM (1, N) model and carried out an experiment on the designed network security situation prediction (NSSP) model in a simulated network environment. It was found that the predicted result of the improved GM (1, N) model was closer to the actual value. Taking the 11th hour as an example, the predicted value of the improved GM (1, N) model was 28.1524, which was only 0.8983 larger than the actual value; compared with neural network and Markov models, the error of the improved GM (1, N) model was smaller: the average error was only 2.3811, which was 67.88% and 70.31% smaller than the other two models. The improved GM (1, N) model had a time complexity that was 49.99% and 39.53% lower than neural network and Markov models; thus, it had high computational efficiency. The experimental results verify the effectiveness of the improved GM (1, N) model in solving the NSSP problem. The improved GM (1, N) model can be further promoted and applied in practice and deployed in the network of schools and enterprises to achieve network information security.

中文翻译:

网络信息安全的灰色关联分析与态势预测研究

为了进一步提高网络信息安全态势预测的准确性和效率,本研究采用基于灰色关联分析的动态等维方法对GM(1,N)模型进行了改进,并对设计的网络安全性进行了实验。模拟网络环境中的情境预测(NSSP)模型。发现改进的GM(1,N)模型的预测结果更接近于实际值。以第11个小时为例,改进后的GM(1,N)模型的预测值为28.1524,仅比实际值大0.8983。与神经网络和马尔可夫模型相比,改进后的GM(1,N)模型的误差较小:平均误差仅为2.3811,比其他两个模型分别小67.88%和70.31%。改良后的通用汽车(1,N)模型的时间复杂度比神经网络和Markov模型低49.99%和39.53%;因此,它具有很高的计算效率。实验结果证明了改进的GM(1,N)模型在解决NSSP问题方面的有效性。改进后的GM(1,N)模型可以进一步推广并在实践中应用,并部署在学校和企业的网络中,以实现网络信息安全。
更新日期:2021-04-20
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