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In this paper, a kind of local outlier mining method based on differentiated cluster center offset measure is proposed through which the outlier degree of sample can be calculated by use of the normal behavior model constructed by normal data sample and the preset anomaly threshold value, and whether the testing sample belong to intrusion behavior can thus be determined. Furthermore, KDD99 data set is also utilized to test the said method, and the experimental results show that the method proposed in this paper possesses higher detection rate and lower false alarm rate

Research on the Intrusion Detection Method Based on Differentiated Cluster Center Offset Measure