Efficient Integration Method of Large-Scale Heterogeneous Security Logs Using NoSQL in Cloud Computing Environment

被引:4
|
作者
Jeong, Huijin [1 ]
Piao, Xuefeng [2 ]
Choi, Junho [3 ]
Shin, Juhyun [4 ]
Kim, Pankoo [5 ]
机构
[1] Korea Elect Safety Corp KESCO, Dept Informat Syst, Seoul, South Korea
[2] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai Campus, Harbin, Peoples R China
[3] Chosun Univ, Div Undeclared Majors, Gwangju, South Korea
[4] Chosun Univ, Dept Control & Measuring Robot Engn, Gwangju, South Korea
[5] Chosun Univ, Dept Comp Engn, Gwangju, South Korea
来源
JOURNAL OF INTERNET TECHNOLOGY | 2016年 / 17卷 / 02期
基金
新加坡国家研究基金会;
关键词
Security log integration; Cloud computing; NoSQL; HBase; MapReduce; MODEL;
D O I
10.6138/JIT.2016.17.2.20150703a
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The cloud computing environment has expanded considerably with the rapid advancement of related technologies. Although cloud computing is convenient for users, detecting and preventing possible security breaches remains an unsolved problem. Security logs are critical data that indicate events in an operating system or other software, and these data are stored through heterogeneous machines such as network security devices, server systems, and database management systems (DBMS). However, existing methods can create problems for efficient analysis because of large-scale heterogeneous security logs in the cloud-computing environment. Therefore, because cloud computing provides various services to users, an efficient integration method of security logs must be developed. This study proposes a NoSQL-based method to collect and integrate security logs using MapReduce. Our study shows that log data were reduced by more than 87% when integrating duplicate large-scale security logs. This proposed method provides faster data storage than conventional DBMS and is more effective.
引用
收藏
页码:267 / 275
页数:9
相关论文
共 50 条
  • [1] A wearable computing environment for the security of a large-scale factory
    Huang, Jiung-yao
    Tsai, Chung-Hsien
    HUMAN-COMPUTER INTERACTION, PT 2, PROCEEDINGS, 2007, 4551 : 1113 - +
  • [2] High efficient training method of MiniGo on large-scale heterogeneous computing platform
    Li, Rongchun
    He, Zhouyu
    Qiao, Peng
    Jiang, Jingfei
    Dou, Yong
    Li, Dongsheng
    Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology, 2024, 46 (05): : 209 - 218
  • [3] An Efficient Organization Method for Large-Scale and Long Time-Series Remote Sensing Data in a Cloud Computing Environment
    Yan, Jining
    Liu, Yuanxing
    Wang, Lizhe
    Wang, Zhipeng
    Huang, Xiaohui
    Liu, Hong
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 9350 - 9363
  • [4] LARGE-SCALE RANKING AND SELECTION USING CLOUD COMPUTING
    Luo, Jun
    Hong, L. Jeff
    PROCEEDINGS OF THE 2011 WINTER SIMULATION CONFERENCE (WSC), 2011, : 4046 - 4056
  • [5] An Empirical Failure-Analysis of a Large-Scale Cloud Computing Environment
    Garraghan, Peter
    Townend, Paul
    Xu, Jie
    2014 IEEE 15TH INTERNATIONAL SYMPOSIUM ON HIGH-ASSURANCE SYSTEMS ENGINEERING (HASE), 2014, : 113 - 120
  • [6] Process virtualization of large-scale lidar data in a cloud computing environment
    Guan, Haiyan
    Li, Jonathan
    Zhong, Liang
    Yu, Yongtao
    Chapman, Michael
    COMPUTERS & GEOSCIENCES, 2013, 60 : 109 - 116
  • [7] Large-scale parallel genome assembler over cloud computing environment
    Das, Arghya Kusum
    Koppa, Praveen Kumar
    Goswami, Sayan
    Platania, Richard
    Park, Seung-Jong
    JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2017, 15 (03)
  • [8] MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous Computing System
    Qiao, Peng
    He, Zhouyu
    Li, Rongchun
    Jiang, Jingfei
    Dou, Yong
    Li, Dongsheng
    2022 IEEE 28TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS, ICPADS, 2022, : 475 - 482
  • [9] Large-scale resource scheduling method using improved genetic algorithm combined with secondary coding in cloud computing environment
    Gu Nan-nan
    Yao Pei-yang
    Jiao Zhi-qiang
    2019 INTERNATIONAL CONFERENCE ON ADVANCED ELECTRONIC MATERIALS, COMPUTERS AND MATERIALS ENGINEERING (AEMCME 2019), 2019, 563
  • [10] A CLOUD COMPUTING PLATFORM FOR LARGE-SCALE FORENSIC COMPUTING
    Roussev, Vassil
    Wang, Liqiang
    Richard, Golden
    Marziale, Lodovico
    ADVANCES IN DIGITAL FORENSICS V, 2009, 306 : 201 - 214