JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (6): 56-63.doi: 10.6040/j.issn.1671-9352.2.2016.216

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Multidimensional data visualization in cloud platform security monitoring

WU Di1,2, WANG Li-na1,2, YU Rong-wei1,2*, ZHANG Xin1,2, XU Lai1,2   

  1. 1. Key Laboratory of Aerospace Informationsecurity and Trusted Computing Ministry of Education, Wuhan University, Wuhan 430072, Hubei, China;
    2. Computer School, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2016-09-02 Online:2017-06-20 Published:2017-06-21

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Abstract: Discussed application method of visualization technology by summarized visualization technology of cloud platform security monitoring and combined with the specific multidimensional monitoring data. Started from the three dimensions of time, node number and performance index type, a visualization method of performance data set based on dimension compression and dimension slice is proposed. Based on this, node self-identification of outliers achieved by used the dynamic time planning and convolution neural network, which extends and riches semantics of the alarm system. It was proved that the method was feasible and could show the effective information more intuitively and improve the decision efficiency of the cloud administrator.

Key words: visualization, outlier node self-identification, cloud security monitoring, cloud security

CLC Number: 

  • TP391
[1] WARD J S, BARKER A. Observing the clouds: a survey and taxonomy of cloud monitoring[J]. Journal of Cloud Computing, 2014, 3(1).
[2] 杨彦波, 刘滨, 祁明月. 信息可视化研究综述[J]. 河北科技大学学报, 2014,35(116): 91-102. YANG Yanbo, LIU Bin, QI Mingyue. Review of imformation visualization[J]. Journal of Hebei University of Science and Technology, 2014, 35(116): 91-102.
[3] 曾悠. 大数据时代背景下的数据可视化概念研究[D]. 杭州:浙江大学, 2014. ZENG You. The concept study of data visualization under the background of big data[D]. Hangzhou: Zhejiang University, 2014.
[4] 任磊, 杜一, 马帅,等. 大数据可视分析综述[J]. 软件学报, 2014(9):1909-1936. REN Lei, DU Yi, MA Shuai, et al. Visual analytics towards big data[J]. Journal of Software, 2014(9):1909-1936.
[5] 张晏. OpenStack的拓扑可视化监控技术研究与实现[D]. 合肥;电子科技大学, 2014. ZHANG Yan. The research and realization of topology visualization monitering technology for openstack[D]. Hefei: University of Electronic Science and Technology of China, 2014.
[6] 周儒军. 基于Hadoop的分布式监控平台的研究与实现[D]. 广州:华南理工大学, 2015. ZHOU Rujun. The research and implementation of distributed monitoring platform based on hadoop[D]. Guangzhou: South China University of Techno- logy, 2015.
[7] 德琨. 基于OpenStack云平台的计算资源监控系统的设计与实现[D]. 北京:北京大学, 2014. DE Kun. Design and implementation of compu- ting resource monitor system based on open- stack cloud platform[D]. Beijing: Peking University, 2014.
[8] 韩敏娜, 刘渊, 陈烨. 基于集对分析的网络安全态势评估[J]. 计算机应用研究, 2012(10):3824-3827. HAN Minna, LIU Yuan, CHEN Ye. Network security situational awareness model based on set pair analysis[J]. Application Research of Computers, 2012(10):3824-3827.
[9] 张勇, 谭小彬, 崔孝林,等. 基于Markov博弈模型的网络安全态势感知方法[J]. 软件学报, 2011(3):495-508. ZHANG Yong, TAN Xiaobin, CUI Xiaolin, et al. Network security situation awareness approa- ch based on markov game model[J]. Journal of Software, 2011(3):495-508.
[10] YURCIK W. NVisionIP and VisFlowConnect-IP: Two tools for visualizing netflows for security[J]. ResearchGate,.
[11] DASGUPTA A, CHEN M, KOSARA R. Conceptualizing visual uncertainty in parallel coordinates[J]. Computer Graphics Forum, 2012, 31(3pt2):1015-1024.
[12] MUELDER C, ZHU B, CHEN W, et al. Visual analysis of cloud computing performance using behavioral lines[J]. IEEE Transactions on Visualization and Computer Graphics, 2016.
[13] 徐波, 于劲松, 李行善. 基于路径约束的动态时间规整方法研究[J]. 系统工程与电子技术, 2004(1):103-105. XU Bo, YU Jinsong, LI Xingshan. Research of dynamic time warping approach based on path constraint[J]. Systems Engineering and Electronics, 2004(1):103-105.
[14] 刘建伟, 刘媛, 罗雄麟. 深度学习研究进展[J]. 计算机应用研究, 2014,31(7):1921-1930. LIU Jianwei, LIU Yuan, LUO Xionglin. Research and development on deep learning[J]. Application Research of Computers, 2014, 31(7):1921-1930.
[15] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理, 2016,31(1):1-17. LU Hongtao, ZHANG Qinchuan. Applications of deep convolutional neural network in computer vision[J]. Journal of Data Acquisition and Processing, 2016, 31(1):1-17.
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