摘要
提出了一种基于态势感知的医院信息系统网络安全模型。融合贝叶斯时序分析、交叉熵径向基函数(CE-RBF)神经网络与图神经网络(GNN)等智能方法,有效提升安全监测与威胁预测能力。实验结果表明,该模型在攻击检测精度、误报率及响应时间等性能指标上均优于传统方法,尤其在满足GB/T 22239—2019等保2.0合规性要求方面表现突出,为医院信息安全提供了一套高效、智能的防护解决方案。
A network security model for hospital information systems based on situation awareness is proposed.By integrating intelligent methods such as Bayesian time series analysis,cross-entropy radial basis function(CE-RBF)neural network and graph neural network(GNN),the capabilities of security monitoring and threat prediction are effectively enhanced.The experimental results show that this model outperforms traditional methods in terms of performance indicators such as attack detection accuracy,false alarm rate and response time.Especially,it performs outstandingly in meeting the compliance requirements of GB/T 22239—2019 Equal Protection 2.0,which provides an efficient and intelligent protection solution for hospital information security.
作者
余映慧
卢幼君
YU Yinghui;LU Youjun(The Fifth Affiliated Hospital of Guangzhou Medical University,Guangzhou 510700,China)
出处
《电子质量》
2025年第12期47-52,共6页
Electronics Quality
