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A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System
Adelaide, Australia January 23-January 24
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.110First International Workshop on Knowl ...
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Hu Zhengbing, Zhou Ji, Ma Ping, "A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System," International Workshop on Knowledge Discovery and Data Mining, pp. 499-502, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.
 
 
BibTex
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@article{ 10.1109/WKDD.2008.110,
author = {Hu Zhengbing and Zhou Ji and Ma Ping},
title = {A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System},
journal ={International Workshop on Knowledge Discovery and Data Mining},
volume = {0},
year = {2008},
isbn = {0-7695-3090-7},
pages = {499-502},
doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.110},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - International Workshop on Knowledge Discovery and Data Mining
TI - A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System
SN - 0-7695-3090-7
SP499
EP502
A1 - Hu Zhengbing,
A1 - Zhou Ji,
A1 - Ma Ping,
PY - 2008
VL - 0
JA - International Workshop on Knowledge Discovery and Data Mining
ER -
 
In this paper, a new method of detector generation and matching mechanism for Negative Selection Algorithm(NSA )is introduced with variable properties, which are called the Nsa-Vs-Detector. The detectors can be variable in different ways using this concept, the paper describes an algorithm when the variable parameter is the size of the detectors in real-valued space. The algorithm is tested with a synthetic datasets, the new method improves the NSA's efficiency and reliability without significant increase in complexity.
Citation:
Hu Zhengbing, Zhou Ji, Ma Ping, "A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System," wkdd, pp.499-502, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008
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