期刊文献+

虚拟化网络中的异常大数据区域挖掘平台设计与改进 被引量:1

Design and Improvement of Abnormal Large Data Area Mining Platform in Virtual Network
在线阅读 下载PDF
导出
摘要 互联网在为我们的生活带来便捷的同时,也带来了对信息安全的新的挑战,而传统技术手段已经无法有效应对。本文在对多源异构的海量数据进行收集的基础上,综合运用多种技术手段,对虚拟网络中异常数据的挖掘平台展开设计,并在现有设计上进行改进,以提高大数据信息的安全。 The Intemet brings convenience to our lives at the same time, but also brings new challenges to information security, and traditional technology has been unable to effectively deal with. In this paper, based on the collection of massive heterogeneous data, we use a variety of technical means to design the mining platform of abnormal data in virtual network and improve it in the existing design to improve the large data Safety.
作者 缪彦深
出处 《数字技术与应用》 2017年第9期171-172,175,共3页 Digital Technology & Application
关键词 大数据 异常 设计 large data anomaly design
  • 相关文献

参考文献4

二级参考文献29

  • 1Lee W, Stolfo S. Data mining approaches for intrusion detection[C]. San Antonio, TX: Proc. 7th USENIX Security Symposium(SECURITY′98). 1998.79-94.
  • 2Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large database[C]. Washington DC:Proceedings of the ACM SIGMOD Conference on Management of Data. 1993.207-216.
  • 3Han J, Fu Y. Discovery of multiple-level association rules from large databases[C]. Proceedings of the 21st Very Large Data Bases Conferences, Zurich, Switzerland, 1995.
  • 4Ross Quinlan J. C4.5 programs for machine learning [Z].Morgan Kaufmann, 1993.
  • 5http:∥www. ll.mit.edu/IST/ideval/.
  • 6Bridges S, Vaughn R. Fuzzy data mining and genetic algorithms applied to intrusion detection[C]. Proc. 23rd National Information Systems Security Conf. Baltimore, MA,2000.
  • 7Shi F. Genetic algorithms for feature selection in an intrusion detection application, masters thesis [C]. Mississippi State University, Mississippi State, MS, 2000.
  • 8Eleazar Eskin, Andrew Arnold, Michael Prerau,et al. A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabled data[Z]. Data Mining for Securiyt Application(DMSA-2002), Kluwer, 2002.
  • 9Marina Thottan, Chuanyi Ji. Anomaly Detection in IP Networks[J].IEEE Transaction On Signal Processing, 2003,51(8): 2191-2204.
  • 10Heikki Mannila, Hannu Toivonen, A.Inkeri Verkamo. Discovery of Ftrecluent Episodes in Event Secluences[C]// Data Mining and Knowledge Discovery. 1997: 259-289.

共引文献5

同被引文献39

引证文献1

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部