Information fusion for intrusion detection

Nong Ye, Mingming Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Intrusion detection is to monitor and capture intrusions into computer and network systems that attempt to compromise the security of computer and network systems. Different intrusion detection techniques exist to evaluate the likelihood of observed activities as a part of an intrusion. When applied to the same observed activities of computer and network systems, different intrusion detection techniques yield different evaluation results. An information fusion technique is required to fuse different results of various intrusion detection techniques for producing a composite value of intrusion likelihood. This paper examines three information fusion techniques based on artificial neural network, linear regression, and logistic regression. These information fusion techniques are compared with respect to their performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International Conference on Information Fusion, FUSION 2000
PublisherIEEE Computer Society
Volume2
DOIs
StatePublished - 2000
Event3rd International Conference on Information Fusion, FUSION 2000 - Paris, France
Duration: Jul 10 2000Jul 13 2000

Other

Other3rd International Conference on Information Fusion, FUSION 2000
CountryFrance
CityParis
Period7/10/007/13/00

Keywords

  • artificial neural network
  • Information fusion
  • intrusion detection
  • linear regression
  • logistic regression

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Ye, N., & Xu, M. (2000). Information fusion for intrusion detection. In Proceedings of the 3rd International Conference on Information Fusion, FUSION 2000 (Vol. 2). [859878] IEEE Computer Society. https://doi.org/10.1109/IFIC.2000.859878