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  • 标题:Intrusion Detection System Using Hierarchical GMM and Dimensionality Reduction
  • 本地全文:下载
  • 作者:L. Maria Michael ; J. Indra Mercy ; N.R. Rejin Paul
  • 期刊名称:International Journal of Computer Applications and Information Technology
  • 印刷版ISSN:2278-7720
  • 出版年度:2012
  • 卷号:1
  • 期号:1
  • 页码:29-33
  • 出版社:Mahadev Educational Society
  • 摘要:The focus of this chapter is to provide the effective intrusion detection technique to protect Web server. The IDS protects an server from malicious attacks from the Internet if someone tries to break in through the firewall and tries to have access on any system in the trusted side and alerts the system administrator in case there is a breach in security. Gaussian Mixture Models (GMMs) are among the most statistically mature methods for clustering the data. Intrusion detection can be divided into anomaly detection and misuse detection. Misuse detection model is to collect behavioral features of non-normal operation and establish related feature library. In the existing system of anomaly based Intrusion Detection System, the work is based on the number of attacks on the network and using decision tree analysis for rule matching and grading. We are proposing an IDS approach that will use signature based and anomaly based identification scheme. And we are also proposing the rule pruning scheme with GMM(Gaussian Mixture Model). It does facilitate efficient way of handling large amount of rules. And we are planned to compare the performance of the IDS on different models. The Dimension Reduction focuses on using information obtained KDD Cup 99 data set for the selection of attributes to identify the type of attacks. The dimensionality reduction is performed on 41 attributes to 14 and 7 attributes based on Best First Search method and then apply the two classifying Algorithms ID3 and J48 Keywords-Intrusion detection, reliable networks, malicious routers, internet dependability, tolerance
  • 关键词:ID3; KDD; IDS; Dimensionality Reduction; NIDS.
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