用可解释AI识别6G医疗物联网漏洞，提升安全与信任
Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks
- 引入SHAP、LIME等可解释AI分析6G医疗设备安全风险
- 实验验证能有效发现系统漏洞并增强防御能力
- 适合关注医疗AI安全的开发者与医疗机构
随着医疗系统越来越多采用先进无线网络和联网设备，保障医疗应用安全变得至关重要。智能医疗物联设备如机器人手术工具、重症监护系统和可穿戴监测仪虽提升了患者护理水平，但也带来了严重的安全风险。针对这些设备的网络攻击可能导致手术错误、设备故障和数据泄露等致命后果。尽管国际电信联盟IMT-2030愿景强调6G通过人工智能与云集成在医疗领域的变革作用，也带来了新的安全挑战。本文探讨了可解释AI技术（如SHAP、LIME、DiCE）在揭示漏洞、强化防御以及提升6G医疗系统中信任与透明度方面的潜力。研究通过实验分析验证了该方法的有效性，并展示了有前景的结果。
原文摘要 · Abstract (English)
As healthcare systems increasingly adopt advanced wireless networks and connected devices, securing medical applications has become critical. The integration of Internet of Medical Things devices, such as robotic surgical tools, intensive care systems, and wearable monitors has enhanced patient care but introduced serious security risks. Cyberattacks on these devices can lead to life threatening consequences, including surgical errors, equipment failure, and data breaches. While the ITU IMT 2030 vision highlights 6G's transformative role in healthcare through AI and cloud integration, it also raises new security concerns. This paper explores how explainable AI techniques like SHAP, LIME, and DiCE can uncover vulnerabilities, strengthen defenses, and improve trust and transparency in 6G enabled healthcare. We support our approach with experimental analysis and highlight promising results.
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