计算机工程与应用2024，Vol.60Issue(10)：140-147,8.DOI:10.3778/j.issn.1002-8331.2301-0087
知识增强的自监督表格数据异常检测方法研究
Self-Supervised Tabular Data Anomaly Detection Method Based on Knowledge Enhancement
摘要
Abstract
The traditional supervised anomaly detection methods have developed rapidly.In order to reduce the depen-dence on labels,self-supervised pre-training methods are widely studied,and the studies show that additional intrinsic semantic knowledge embedding is crucial for table learning.In order to mine the rich knowledge information in tabular data,the self-supervised tabular data anomaly detection method based on knowledge enhancement(STKE)is proposed with the following improvements.The proposed data processing module integrates domain knowledge(semantics)and statistical mathematics knowledge into feature construction.At the same time,self-supervised pre-training(parameter learning)provides contextual knowledge priors to achieve the rich information transfer of tabular data.The mask mecha-nism is used on the original data to learn the masked features by learning the relevant non-masked features,and predict the original value of the additive Gaussian noise in the hidden layer space of the data.This strategy promotes the model even in the presence of noisy inputs.The original feature information can also be recovered.A hybrid attention mecha-nism is used to effectively extract association information between data features.The experimental results of the proposed method on six datasets show superior performance.关键词
异常检测/自监督/知识增强/预训练Key words
anomaly detection/self-supervised/knowledge enhancement/pre-training分类
信息技术与安全科学引用本文复制引用
高小玉,赵晓永,王磊..知识增强的自监督表格数据异常检测方法研究[J].计算机工程与应用,2024,60(10):140-147,8.基金项目
国家重点研发计划(2019YFB1705402) （2019YFB1705402）
教育部人文社科规划基金项目(20YJAZH129) （20YJAZH129）
北京市教育委员会社科计划重点项目(SZ202011232024). （SZ202011232024）
