DocumentCode :
2292943
Title :
Information fusion for intrusion detection
Author :
Ye, Nong ; Xu, Mingming
Author_Institution :
Dept. of Ind. Eng., Arizona State Univ., Tempe, AZ, USA
Volume :
2
fYear :
2000
fDate :
10-13 July 2000
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.
Keywords :
security of data; sensor fusion; artificial neural network; information fusion techniques; intrusion detection; linear regression; logistic regression; network systems; Artificial neural networks; Computer networks; Computer security; Computerized monitoring; Decision trees; Industrial engineering; Information security; Intrusion detection; Linear regression; Logistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Conference on
Conference_Location :
Paris, France
Print_ISBN :
2-7257-0000-0
Type :
conf
DOI :
10.1109/IFIC.2000.859878
Filename :
859878
Link To Document :
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