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D-S Evidence Theory and its Data Fusion Application in Intrusion Detection
Dalian, China December 05-December 08
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PDCAT.2005.109Sixth International Conference on Par ...
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Junfeng Tian, Weidong Zhao, Ruizhong Du, Zhe Zhang, "D-S Evidence Theory and its Data Fusion Application in Intrusion Detection," Parallel and Distributed Computing Applications and Technologies, International Conference on, pp. 115-119, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005.
 
 
BibTex
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@article{ 10.1109/PDCAT.2005.109,
author = {Junfeng Tian and Weidong Zhao and Ruizhong Du and Zhe Zhang},
title = {D-S Evidence Theory and its Data Fusion Application in Intrusion Detection},
journal ={Parallel and Distributed Computing Applications and Technologies, International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2405-2},
pages = {115-119},
doi = {http://doi.ieeecomputersociety.org/10.1109/PDCAT.2005.109},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Parallel and Distributed Computing Applications and Technologies, International Conference on
TI - D-S Evidence Theory and its Data Fusion Application in Intrusion Detection
SN - 0-7695-2405-2
SP115
EP119
A1 - Junfeng Tian,
A1 - Weidong Zhao,
A1 - Ruizhong Du,
A1 - Zhe Zhang,
PY - 2005
KW - null
VL - 0
JA - Parallel and Distributed Computing Applications and Technologies, International Conference on
ER -
 
Junfeng Tian, Hebei University, Baoding, China
Weidong Zhao, Hebei University, Baoding, China
Ruizhong Du, Hebei University, Baoding, China
Zhe Zhang, Hebei University, Baoding, China
Based on the D-S Evidence Theory and its Data Fusion technology, a new Intrusion Detection Data Fusion Model-IDSDFM is presented. This model can merge alerts of different types of IDSs, make intelligent inference by applying the D-S Evidence Theory, and estimate the current security situation according to the fusion result. Then some IDSs in the network are dynamically adjusted to strengthen the detection of the data that relate to the attack attempt. Consequently, the false positive rate and the false negative rate are effectively reduced, and the detection efficiency of IDS is accordingly improved.
Citation:
Junfeng Tian, Weidong Zhao, Ruizhong Du, Zhe Zhang, "D-S Evidence Theory and its Data Fusion Application in Intrusion Detection," pdcat, pp.115-119, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005
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