Information fusion for intrusion detection

Nong Ye; Mingming Xu

doi:10.1109/IFIC.2000.859878

Information fusion for intrusion detection

Nong Ye, Mingming Xu

Industrial, Systems and Operations Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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 language	English (US)
Title of host publication	Proceedings of the 3rd International Conference on Information Fusion, FUSION 2000
Publisher	IEEE Computer Society
Volume	2
DOIs	https://doi.org/10.1109/IFIC.2000.859878
State	Published - 2000
Event	3rd International Conference on Information Fusion, FUSION 2000 - Paris, France Duration: Jul 10 2000 → Jul 13 2000

Other

Other	3rd International Conference on Information Fusion, FUSION 2000
Country/Territory	France
City	Paris
Period	7/10/00 → 7/13/00

Keywords

artificial neural network
Information fusion
intrusion detection
linear regression
logistic regression

ASJC Scopus subject areas

Information Systems

Access to Document

10.1109/IFIC.2000.859878

Cite this

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title = "Information fusion for intrusion detection",

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 = "artificial neural network, Information fusion, intrusion detection, linear regression, logistic regression",

author = "Nong Ye and Mingming Xu",

year = "2000",

doi = "10.1109/IFIC.2000.859878",

language = "English (US)",

volume = "2",

booktitle = "Proceedings of the 3rd International Conference on Information Fusion, FUSION 2000",

publisher = "IEEE Computer Society",

note = "3rd International Conference on Information Fusion, FUSION 2000 ; Conference date: 10-07-2000 Through 13-07-2000",

}

TY - GEN

T1 - Information fusion for intrusion detection

AU - Ye, Nong

AU - Xu, Mingming

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

KW - artificial neural network

KW - Information fusion

KW - intrusion detection

KW - linear regression

KW - logistic regression

UR - http://www.scopus.com/inward/record.url?scp=68949175922&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=68949175922&partnerID=8YFLogxK

U2 - 10.1109/IFIC.2000.859878

DO - 10.1109/IFIC.2000.859878

M3 - Conference contribution

AN - SCOPUS:68949175922

VL - 2

BT - Proceedings of the 3rd International Conference on Information Fusion, FUSION 2000

PB - IEEE Computer Society

T2 - 3rd International Conference on Information Fusion, FUSION 2000

Y2 - 10 July 2000 through 13 July 2000

ER -

Information fusion for intrusion detection

Abstract

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Keywords

ASJC Scopus subject areas

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