基于方差特征选择与3DES加密算法的医院信息数据安全防御系统设计

Design of Hospital Access Control System Based on Machine Learning

摘要:

常规的医院信息安全防护主要采用信息属性特征融合分类的方法进行安全防护,忽略了信息存储逻辑节点拓扑结构造成的安全影响,导致安全防护攻击成功概率较高。因此,本文对医院访问控制系统进行了研究。提出采用机器学习中的改进方差特征法对医院访问控制系统进行优化设计。在硬件设计部分,对医院访问控制终端、控制终端与人机接口和接口数据传输模块进行设计；在医院访问控制方案设计中,采用方差特征选择方法对医院信息进行过滤和归一化处理完成数据更新；采用3DES加密算法,建立信息的模糊判断矩阵,完成医院信息安全访问控制系统设计。经过实验测试本文设计的系统可有效降低各种攻击的成功概率,平均攻击成功率仅为2.33%,安全性较高,有效保障医院信息安全,避免医院信息的泄露或故障。

Abstract:

The conventional hospital information security protection mainly adopts the method of information attribute feature fusion classification for security protection, ignoring the security impact caused by the topological structure of information storage logical nodes, resulting in a high probability of successful security protection attacks. Therefore, this article conducted research on hospital access control systems. Propose using the improved variance feature method in machine learning to optimize the design of hospital access control systems. In the hardware design section, design the hospital access control terminal, control terminal and human-machine interface, and interface data transmission module; In the design of hospital access control schemes, the variance feature selection method is used to filter and normalize hospital information to complete data updates; Using 3DES encryption algorithm, establish a fuzzy judgment matrix for information, and complete the design of hospital information security access control system. After experimental testing, the system designed in this article can effectively reduce the success probability of various attacks, with an average attack success rate of only 2.33%. It has high security and effectively ensures the security of hospital information, avoiding leakage or failure of hospital information.