计算机应用与软件2024，Vol.41Issue(8)：367-375,9.DOI:10.3969/j.issn.1000-386x.2024.08.052
面向类重叠日志的一致性异常检测模型
CONFORMAL ANOMALY DETECTION MODEL FOR CLASS OVERLAP LOGS
摘要
Abstract
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.关键词
异常检测/类重叠/一致性检测/模糊度/置信度Key words
Anomaly detection/Class overlap/Conformal detection/Fuzzy degree/Confidence分类
计算机与自动化引用本文复制引用
吕宗平,梁孟孟,顾兆军,刘春波,王志..面向类重叠日志的一致性异常检测模型[J].计算机应用与软件,2024,41(8):367-375,9.基金项目
国家自然科学基金项目(61872202,61601467) （61872202,61601467）
民航安全能力建设项目(PESA2019073,PESA2019074) （PESA2019073,PESA2019074）
中国科学院重点部署项目(KFZD-SW-440) （KFZD-SW-440）
天津市自然科学基金项目(19JCYBJC15500). （19JCYBJC15500）
