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移动网络隐私信息库未知访问源安全性预警

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对互联网信息安全预警过程中，受信息数据规模大、种类多影响，导致预警精度低、耗时长的问题，为提高预警效率，提出移动网络隐私信息库未知访问源安全性预警。利用主成分分析法对信息库数据进行降维处理，降低检测难度;利用迭代多元自回归预测(IMAP:Iterative Multivariate AutoRegressive Modelling and Prediction)算法进行数据聚类处理，提取离散性孤立数据点，完成信息库未知访问源数据筛查。将未知访问源数据输入到支持向量机中，利用时间窗口将信息库安全预警模型的构建问题转化为支持向量机学习的凸优化问题，输出安全性预警结果，并对预警模型的构建参数进行全局寻优，提高安全预警模型的预警输出能力。实验结果表明，所提方法对信息库的安全检测效率较高，且面对多类型信息库入侵攻击能做到稳定、精准预警输出。
Unknown Access Source Security Alert of Mobile Network Privacy Information Base
Due to the large scale and variety of information data in the process of internet information security warning,the warning accuracy is low and the time is long.To improve the efficiency of early warning,a security warning for unknown access sources in mobile network privacy information databases is proposed.Principal component analysis method is used to reduce the dimensionality of information base data to reduce the difficulty of detection.The IMAP(Iterative Multivariate AutoRegressive Modelling and Prediction)algorithm is used to carry out data clustering processing,to extract discrete isolated data points,and complete the screening of unknown access source data in the information base.Unknown access source data is inputted into a support vector machine,a time window is used to transform the construction problem of the information base security warning model into a convex optimization problem of support vector machine learning.Security warning results are outputted,and globally optimize the construction parameters of the warning model are optimized to improve the warning output ability of the security warning model.The experimental results show that the proposed method has high security detection efficiency for information databases,and can achieve stable and accurate warning output in the face of multiple types of information database intrusion attacks.

principal component analysis methoditerative multivariate autoregressive modelling and prediction(IMAP)clustering methodtime windowsupport vector machine learning methodconvex optimization problem

曹敬馨、刘洲洲

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西安航空学院计算机学院,西安 710077

主成分分析法 IMAP聚类法 时间窗口 支持向量机学习法 凸优化问题

陕西省重点研发计划基金资助项目

2023-YBGY-014

2024

吉林大学学报(信息科学版)
吉林大学

吉林大学学报(信息科学版)

CSTPCD
影响因子：0.607
ISSN：1671-5896
年,卷(期)：2024.42(4)