An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response

Ruirui Zhang and Xin Xiao
Volume: 15, No: 1, Page: 137 ~ 150, Year: 2019
10.3745/JIPS.03.0108
Keywords: Antibody Concentration, Artificial Immune, Cloud Model, Evolutionary Algorithms, Intrusion Detection
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Abstract
Although the research of immune-based anomaly detection technology has made some progress, there are still some defects which have not been solved, such as the loophole problem which leads to low detection rate and high false alarm rate, the exponential relationship between training cost of mature detectors and size of selfantigens. This paper proposed an intrusion detection method based on changes of antibody concentration in immune response to improve and solve existing problems of immune based anomaly detection technology. The method introduces blood relative and blood family to classify antibodies and antigens and simulate correlations between antibodies and antigens. Then, the method establishes dynamic evolution models of antigens and antibodies in intrusion detection. In addition, the method determines concentration changes of antibodies in the immune system drawing the experience of cloud model, and divides the risk levels to guide immune responses. Experimental results show that the method has better detection performance and adaptability than traditional methods.

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Cite this article
IEEE Style
R. Z. X. Xiao, "An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response," Journal of Information Processing Systems, vol. 15, no. 1, pp. 137~150, 2019. DOI: 10.3745/JIPS.03.0108.

ACM Style
Ruirui Zhang and Xin Xiao. 2019. An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response, Journal of Information Processing Systems, 15, 1, (2019), 137~150. DOI: 10.3745/JIPS.03.0108.