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  • 标题:Survey of Network Intrusion Detection Using K-Mean Algorithm
  • 本地全文:下载
  • 作者:Poonam Dabas ; Rashmi Chaudhary
  • 期刊名称:International Journal of Advanced Research In Computer Science and Software Engineering
  • 印刷版ISSN:2277-6451
  • 电子版ISSN:2277-128X
  • 出版年度:2013
  • 卷号:3
  • 期号:3
  • 出版社:S.S. Mishra
  • 摘要:Intrusion Detection System (IDS) due to novel attack method or upgraded. Because many current IDSs are constructing by point instruction of professional knowledge, changes to IDSs are costly and slow. Intrusion detection techniques can be ca tegorize into irregularity detection and mistreat detection. Anomaly detection systems, for example, IDES Intrusion detection systems (IDS) process large amount of monitoring data. As an illustration, host-based IDS examine log les on a computer (or host) in classify to identify suspicious activities. Network-based IDS, on the other hand, searches network monitor data for harmful packets or packet flows. In the behind 1990s, development in data mining research and the essential to find recovered methods for network and host based intrusion detection resulted in research activities attempting to organize data mining techniques for anomaly and attack detection. IDS may be a straightforward assessment trail process, or a filter process using a traffic control system, like screening routers, packet filters, firewalls, etc. It is an major technology in business sector as well as an dynamic area of research. In Information Security, intrusion detection is the proceed of detecting proceedings that attempt to cooperate the confidentiality, integrity or availability of a resource. It acting a particularly significant role in attack detection, security check and network inspect
  • 关键词:Instruction Detection System; K-Mean Algorithm; Data Clustering; Machine Learning
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