Volume 9 Number 3 (Mar. 2014)
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JSW 2014 Vol.9(3): 785-791 ISSN: 1796-217X
doi: 10.4304/jsw.9.3.785-791

The Clonal Expansion and Memory Strategy Applied to Network Detection

Yuling Tian
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan shanxi 030024, China

Abstract—The dynamic tendency of network environment determines that system can achieve an accurate fault diagnosis only by self-learning. Inspired by characters of artificial immune and adaptability of dynamic clonal selection algorithm for dynamic environment, an immune algorithm applied to network fault diagnosis was proposed based on the detector population quality and the memory characteristics. The clonal expansion strategy was designed to improve the quality of mature detector populations and the classification memory strategy can achieve dynamic updated memory detector population through evaluating the effectiveness of the memory detectors. The experimental results show that the network fault diagnosis based on immune theory can achieve successive learning to accommodate the emerging new situations, and improve the accuracy rate and efficiency of detecting known and unknown faults.

Index Terms—immune algorithm, clonal expansion, memory classification, fault detection


Cite: Yuling Tian, "The Clonal Expansion and Memory Strategy Applied to Network Detection," Journal of Software vol. 9, no. 3, pp. 785-791, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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