中国医疗设备2024，Vol.39Issue(5)：61-65,5.DOI:10.3969/j.issn.1674-1633.2024.05.011
基于XGBoost算法的医保信息系统入侵安全风险监测方法
Intrusion Security Risk Monitoring Method for Medical Insurance Information Systems Based on XGBoost Algorithm
摘要
Abstract
Objective In view of the complicated judgment process and long monitoring time of conventional medical insurance information system,to propose an intrusion security risk monitoring method based on XGBoost algorithm.Methods According to the relationship of security factors,the security factor correlation degree correction strength was calculated and corrected,and the modified security factor correlation was divided,and then the divided system intrusion security information correlation degree was obtained.According to the classification of correlation degree parameters,the intrusion behavior was classified based on XGBoost algorithm.According to the results,the intrusion behavior information was matched by the tree edit distance algorithm,and the neural network monitoring structure was established.According to the matching result,the corresponding alarm mechanism was established to realize the real-time monitoring of the intrusion security risk of the medical insurance information system.Results The experimental results showed that the average response time of the proposed method for monitoring intrusion security risks in medical insurance information systems was less than 3 s,with a shorter response time and higher security.Conclusion The intrusion security risk monitoring method of medical insurance information system based on XGBoost algorithm proposed in this paper can effectively monitor the intrusion security risk of medical insurance information system,and has high application value.关键词
XGBoost算法/医保信息系统/入侵安全风险监测/行为识别/安全因素关联/树编辑距离算法Key words
XGBoost algorithm/medical insurance information system/intrusion security risk monitoring/behavior identification/safety factor correlation/tree edit distance algorithm分类
医药卫生引用本文复制引用
刘佩..基于XGBoost算法的医保信息系统入侵安全风险监测方法[J].中国医疗设备,2024,39(5):61-65,5.基金项目
山东省优秀中青年科学家科研奖励基金(BS2018SW427). （BS2018SW427）
