DocumentCode :
3102433
Title :
Negative selection algorithm based on immune suppression
Author :
Gui-Yang Li ; Li, Hai-bo ; Zeng, Jie ; Hai-Bo Li
Author_Institution :
Sch. of Comput. Sci., Sichuan Univ., Chengdu, China
Volume :
6
fYear :
2009
fDate :
12-15 July 2009
Firstpage :
3227
Lastpage :
3232
Abstract :
The negative selection algorithm (NSA) is one of models in artificial immune systems. In this paper, two issues existed in traditional NSAs are described. Inspired by immune suppression mechanism, a novel framework of NSA that combining boundary selves and detectors to perform detection is proposed. By introducing the framework into V-detector algorithm, the improved algorithm is implemented and applied with synthetic data and real data. The experiment results show that the new algorithm based on immune suppression ensures better detection performance with fewer detectors.
Keywords :
artificial immune systems; V-detector algorithm; artificial immune systems; immune suppression; negative selection algorithm; Artificial immune systems; Biological system modeling; Cells (biology); Computer science; Cybernetics; Detectors; Fault detection; Immune system; Intrusion detection; Machine learning; Boundary self; Hypothesis testing; Immune suppression; Negative selection; ROC;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
Type :
conf
DOI :
10.1109/ICMLC.2009.5212777
Filename :
5212777
Link To Document :
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