CONFORMAL ANOMALY DETECTION MODEL FOR CLASS OVERLAP LOGS
In system log anomaly detection,the class overlap of decision boundaries makes it difficult for traditional classifiers to achieve correct classification.In order to avoid time-consuming preprocessing techniques or dependence on specific algorithms,a conformal anomaly detection model is proposed.The model calculated the membership degree of samples and different categories,and selected the best fuzzy degree to separate the class overlap logs according to the accuracy difference of the traditional classifier.The p value was obtained by integrating the non-conformal measure function of the ensemble learning classifier,and the class overlapping log labels were obtained according to the preset confidence.Experimental results show that compared with the traditional classifiers,the recall rate and F-measure of the proposed model are increased by about 10 percentage points on average,which verifies the effectiveness of the proposed model in dealing with class overlap.
国家科技期刊平台
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