arXiv · cs.AI · Pranav Patel, Siva Kannathasan, Prerak Patel, Jigar Patel, Vansh Patel, Sheel Patel · 9 小时前模型评分32
面向人工智能（AI，Artificial Intelligence）对抗条件下互联临床技术的临床协议感知网络韧性框架
Clinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial Conditions
导读
一篇投稿IEEE的论文提出基于有界临床微网络与受控互联的临床协议感知网络韧性框架，将临床因素纳入网络安全的设计、管理与评估。参数化蒙特卡洛模拟显示，互联架构使平均网络暴露较分段架构上升56.3%，预防与遏制结合则使暴露下降60.1%；协议依赖度提高可在网络暴露不变时把建模临床影响指数从1.021推高至7.567。作者强调结果仅支持架构对比分析，并非真实网络风险或患者伤害的校准估计。
分类置信度
中 · 待人工复核
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
应用领域：医疗与生命科学
事件类型：研究发布
证据摘录
- “This paper proposes a clinical-protocol-aware cyber-resilience framework based on bounded clinical micro-networks and controlled interconnections.”
- “The findings support comparative architectural analysis, not calibrated estimates of real-world cyber risk, control effectiveness, or patient harm.”
- “Under the simulation assumptions, connected architecture increases mean cyber exposure by 56.3% relative to segmented architecture, while combined prevention and containment reduce exposure by 60.1% relative to no controls.”
来源：arXiv · cs.AI · arxiv.org