Negative Selection Algorithm Based on Double Matching Rules

Abstract:

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The theory of artificial immune had been widely used in the research of network intrusion detection. Nowadays, the existing detector generating algorithms based on negative selection usually use a certain matching rule, as a result, too many detectors may generate, and the false alarm rate will become more serious. This paper proposes an improved negative selection algorithm using double matching rule: candidate detectors should be selected by the improved Hamming distance matching first, then the remaining detectors go through the segmented r-chunks(rch) matching rule. Experiments show that compared with traditional algorithms, this method brings a small number and more efficient detectors, reduces the false alarm rate and guarantees the efficiency of detectors.

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

42-45

DOI:

10.4028/www.scientific.net/AMR.204-210.42

Citation:

Y. Hu and B. Li, "Negative Selection Algorithm Based on Double Matching Rules", Advanced Materials Research, Vols. 204-210, pp. 42-45, 2011

Online since:

February 2011

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Price:

$35.00

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