安全的医疗数据共享和攻击检测框架使用辐射基础神经网络
Abhishek Kumar1, Priya Batta1, Pramod Singh Rathore2
1Department of Computer Science and Engineering, Chandigarh University, Punjab, Mohali, India.
Scientific reports
|May 2, 2025
概括
本研究引入了一个安全的访问控制机制 (PA2C) 和一个攻击检测模型 (IntVO-RBNN),以提高电子健康记录 (EHR) 数据共享的安全性. 新的方法提高了医疗保健应用中的数据完整性和网络安全性.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 医疗保健信息学 医疗保健信息学
背景情况:
- 安全的医疗数据共享至关重要,但目前的架构缺乏对敏感健康信息的全面安全.
- 现有的访问控制方法通常是应用特定的,无法满足医疗保健的动态和复杂的安全需求.
- 医疗保健需要动态的许可执行,上下文意识的访问控制和灵活的身份验证,以确保安全的数据管理.
研究的目的:
- 提出一种新的安全架构,用于在共享和访问过程中保护敏感的医疗数据.
- 开发一种有效的攻击检测模型,用于识别医疗保健系统中的网络威胁.
- 加强电子健康记录 (EHR) 数据共享的完整性,安全性和可靠性.
主要方法:
- 一个拟议的验证访问控制机制 (PA2C) 使用智能合约,加密和安全的密钥管理.
- 基于智能航行优化算法的辐射基础神经网络 (IntVO-RBNN) 用于网络攻击检测.
- 智能航行优化算法用于超参数调整和混合功能,以有效识别攻击模式.
主要成果:
- 该PA2C机制在现有方法中表现出优越的性能,100个块的最小响应时间为100.18秒,信息损失为4.49%.
- IntVO-RBNN模型在攻击检测方面取得了高性能,回忆率为95.26%,精度为97.84%,准确率为94.02%.
- 对比分析证实了拟议的访问控制战略和攻击检测模型的有效性.
结论:
- 该PA2C机制显著提高了电子健康记录数据共享的安全性和可靠性,确保了数据完整性.
- IntVO-RBNN模型为检测医疗保健系统内的网络攻击提供了有效的解决方案.
- 该研究提供了一个强大的安全框架,以解决医疗保健部门复杂和动态的安全需求.
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