An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response
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초록
Although the research of immune-based anomaly detection technology has made some progress, there are stillsome defects which have not been solved, such as the loophole problem which leads to low detection rate andhigh 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 inimmune response to improve and solve existing problems of immune based anomaly detection technology. Themethod introduces blood relative and blood family to classify antibodies and antigens and simulate correlationsbetween antibodies and antigens. Then, the method establishes dynamic evolution models of antigens andantibodies in intrusion detection. In addition, the method determines concentration changes of antibodies inthe immune system drawing the experience of cloud model, and divides the risk levels to guide immuneresponses. Experimental results show that the method has better detection performance and adaptability thantraditional methods.
Abstract
Although the research of immune-based anomaly detection technology has made some progress, there are stillsome defects which have not been solved, such as the loophole problem which leads to low detection rate andhigh 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 inimmune response to improve and solve existing problems of immune based anomaly detection technology. Themethod introduces blood relative and blood family to classify antibodies and antigens and simulate correlationsbetween antibodies and antigens. Then, the method establishes dynamic evolution models of antigens andantibodies in intrusion detection. In addition, the method determines concentration changes of antibodies inthe immune system drawing the experience of cloud model, and divides the risk levels to guide immuneresponses. Experimental results show that the method has better detection performance and adaptability thantraditional methods.
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