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作 者:魏琛[1] 庄子波[1] 刘凤鑫[1] WEI Chen;ZHUANG Zi-bo;LIU Feng-xin(Flight Academy,Civil Aviation University of China,Tianjin 301500,China)
出 处:《计算机仿真》2024年第2期349-353,共5页Computer Simulation
基 金:天津市自然科学基金多元投入基金面上项目(21JCYBJC00740);中国气象局气象软科学项目(2023ZZXM29)。
摘 要:由于大型实验室网络中的敏感数据认证过程繁琐复杂,且对其安全性要求较高,增加用户访问敏感数据的复杂性和时间成本。为此,提出一种大型实验室网络敏感数据访问多阶段认证算法。利用互补集合经验模态分解(Coplementary Ensemble Empirical Mode Decomposition,CEEMD),分解含有噪声的大型实验室网络数据,获取不同本征模态函数,通过改进小波阈值去噪,重构本征模态函数(Intrinsic Mode Functions,IMF)信息,获取降噪处理后的数据。通过Apriori算法和互信息理论,分析不同数据之间的相关性,挖掘其关联性,实现大型实验室网络敏感数据访问多阶段认证。通过仿真分析证实,采用所提算法可以精准完成大型实验室网络敏感数据访问多阶段认证,准确性在90%以上。Due to the cumbersome and complex authentication process of sensitive data in large laboratory networks,as well as the high security requirement,it increased the complexity and time cost for users to acess sensitive data.Therefore,a multi-stage authentication algorithm for accessing sensitive data in large laboratory network was proposed.Firstly,Complementary Ensemble Empirical Mode Decomposition(CEEMD)was used to decompose the data containing noise,thus obtaining different intrinsic mode functions.Secondly,the improved wavelet threshold denoising method was adopted to reconstruct the information of intrinsic mode components(IMF).And then,the data after noise reduction was obtained.Moreover,Apriori algorithm and mutual information theory were used to analyze the correlation and association between different data.Finally,the multi-stage authentication for accessing sensitive data in large laboratory network was achieved.Simulation results prove that the proposed algorithm can accurately complete the multi-stage authentication for accessing sensitive data in large laboratory network,with an accuracy rate of over 90%.
分 类 号:TP392[自动化与计算机技术—计算机应用技术]
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