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
刘佩1
作者信息
- 1. 烟台市心理康复医院 医保办,山东 烟台 265200
- 折叠
摘要
目的 针对常规的医保信息系统对入侵行为的判定过程较为繁杂、监测反应时间较长的问题,提出基于XGBoost算法的医保信息系统入侵安全风险监测方法.方法 按照安全性影响因素的关系,对安全因素关联度修正强度进行计算与修正,并对修正后的安全因素关联进行划分,得到划分的系统入侵安全信息关联度;根据划分关联度参数,基于XGBoost算法对入侵行为进行分级判定;按照结果,使用树编辑距离算法,对入侵行为信息进行匹配,并建立神经网络监测结构;根据匹配结果建立对应报警机制,实现对医保信息系统入侵安全风险的实时监测.结果 实验结果表明,本文方法对医保信息系统入侵安全风险的监测平均反应时间小于3 s,反应时间较短,安全性较高.结论 本文提出的基于XGBoost算法的医保信息系统入侵安全风险监测方法,能够有效监测医保信息系统的入侵安全风险,具有较高的应用价值.
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引用本文复制引用
基金项目
山东省优秀中青年科学家科研奖励基金(BS2018SW427)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据