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利用群组探勘技术搜寻校园问题装置之社会网路

【中文摘要】:友善、方便的网路使用环境与严格控管的网路使用环境一直是网路管理人员经常面对的两难问题;控管严格的网路使用时一定要经过认证或是事先经过注册,但对于临时使用者、外来的访客,突然或经常需要使用网路的新设备装置的使用者,就变成是个困扰。如建构一个对网路装置友善开放易用的网路环境,让使用者只要插上网路线或自动搜寻无线网路即可使用网路,无须认证,虽然方便却也同时也带来管理上的困扰,当使用者不当使用网路或系统被入侵而造成网路的瘫痪时,也不易追蹤找出发生问题的使用者装置。
在此研究的目标是以S大学学院内的网路环境为範例,透过简易网路管理协定收集网路装置的网路卡位址(MAC address)等相关资料,这些装置可能是一般电脑、笔记型电脑、平板电脑、手机、印表机或透过网路控制的资讯设备。但装置资料的维护会因为人员设备的异动、地点转移需要时常更新资料,常造成设备资料缺少的状况。因此希望能够过资料探勘的方式,透过现有资料中可靠度高的装置资讯找出与未知装置间的关连性,让经验不足的管理人员也可以透过资讯系统提供的知识来解决问题。
实验结果显示,此方法可有效地识别清晰的标识设备和其他设备都是高度相关的问题,透过本研究的方法可找出问题装置的接近群组 ,探勘适合人工查核有问题的实体网[custom_fra

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me_center]szwox.com_142[/custom_frame_center]路位址,数量越少装置相关性越高。对于网络管理设备发生问题时有很大的帮助。
【英文摘要】:A friendly, convenience and security are two contradictory goals faced by network managers. When using a strictly controlled network, users must be certified or registered in advance in order to ensure security. However, for temporary users, outsiders, and people who frequently use new devices, the process is often considered troublesome. An open and friendly internet environment allows users to simply plug-in cables or automatically search with wireless and get on internet without authentication yet compromises internet security. In the later environment, when the network is paralyzed by improper use of users or attacks on system, it is not easy to identify the users of the device that causes the problem.
This study takes College network environment of University S as an example and collect MAC address and relative data of various devices through SNMP (Simple Network Management Protocol). These devices may be personal computers, laptop computers, tablet computers, mobile phones, printers or other devices that are controlled through internet. Due to the change of personnel, equipment and their location, the data about a device sometimes is inaccurate and need to be constantly updated. Therefore, In this thesis, I proposes a data mining approach to identifies devices with reliable data in connection with some problematic device whose data is either missing or inaccurate. These related devices may reveal the real identity of the owner of the problematic device.
Is experimental results show identifying clear identification and other equipment are highly related issues. Through the method of this research may identify problems with the devi

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ce close group. Mining suitable for physical network address manually check in question, the fewer the number of devices more relevant. For network management equipment problems, there is a great help.
【参考文献】:

  • 中文文献
  • 1. Pang-Ning Tan, Michael Steinback, Vipin Kumar, 资料探勘, 台北市: 台湾培生教育, 2007.
  • 2. 李光伟, “学生宿舍网路与管理系统,” 2010. [线上]. Available: http://www.cc.ntu.edu.tw/chinese/epaper/0013/20100620_1301.htm.
  • 3. 谢昇晃, “跨层之网路管理系统:以辅仁大学为实例,” 辅仁大学电子工程学系硕士论文, 2009.
  • 4. 潘立人, “资料搜寻系统视觉化与多维度分析之设计:以资讯工程研究論文检索系统为例,” 国立中央大学资讯工程学系硕士論文, 2010.
  • 5. 刘兴宪, “校园网路管理与整合模式之研究-以国立彰化师範大学宿舍网路为例,” 国立彰化师範大学资讯管理学系硕士论文, 2010.
  • 6. 赖意淳, “具自调式能力之网路流量分析系统,” 铭传大学资讯传播工程学系硕士论文, 2009.
  • 7. Douglas Mauro & Kevin Schmidt, “网路管理与S N M P,” 于 SNMP 网路管理实务 第二版, O”Reilly Media, Inc. Taiwan Branch, 2007.
  • 8. 张展华&包苍龙, “旅馆网路系统管理策略之研究,” 于 Workshop on Computer Networks and Web Service/Technologies, 2009.
  • 英文文献
  • 1. Agrawal, R., Imielinski, T., and Swami, A., "Mining Association Rules between Sets of Items in Large Databases," in 1993 ACM SIGMOD Conference, Washington DC, 1993.
  • 2. B. T. LLC, "BRIDGE-MIB," ByteSphere Technologies LLC, 2006. [Online]. Available: http://www.oidview.com/mibs/0/BRIDGE-MIB.html.
  • 3. Y. Wang, E.-P. Lim, S.-Y. Hwang, "Efficient Mining of Group Patterns from User Movement Data," Data and Knowledge Engineering (DKE), vol. 3, no. 57, pp. 240-282, 2006.
  • 4. Rithme, "Knowledge Discovery and Data Mining," 2014. [Online]. Available: http://www.rithme.eu/?m=resources&p=kdprocess&lang=en.
  • 5. Christian Borgelt, "Find Frequent Item Sets with the FP-growth Algorithm," 2013. [Online]. Available: http://www.borgelt.net/doc/fpgrowth/fpgrowth.html.
  • 6. Agrawal, R., & Srikant, R., “Fast algorithms for mining association rules.,” in Proc. 20th Int. Conf., 1994.
  • 7. Christian Borgelt, "FP-growth – Frequent Item Set Mining," 2014. [Online]. Available: http://borgelt.net/fpgrowth.html.
  • 8. W. Nicholas, “RFC1213-MIB MIB Download,” ByteSphere LLC, and Nicholas W. Saparoff., 2012. [Online]. Available: http://www.oidview.com/mibs/0/RFC1213-MIB.html.
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  • 10. A. JACOMY, "SIGMAJS," 2014. [Online]. Available: http://sigmajs.org.
  • 11. S. Waldbusser, "SNMPWALK," Wes Hardaker, 2002. [Online]. Available: http://www.net-snmp.org/docs/man/snmpwalk.html.
  • 12. Han, J., Pei, J., & Yin, Y., "Mining frequent patterns without candidate generation.," ACM SIGMOD Record, vol. 29, no. 2, pp. 1-12, May 2000.
  • 13. Christian Borgelt, "An Implementation of the FP-growth Algorithm," Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations., pp. 1-5, 8 2005.
  • 14. Christian Borgelt, "Frequent item set mining," DATA MINING AND KNOWLEDGE DISCOVERY, vol. 2, p. 437–456, 18 Oct 2012.
  • 来源:中山大学;作者:张明泰
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