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相关概念视频

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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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DC-NFC:一个定制的深度学习框架,用于NFC启用物联网中的安全和隐私.

Abdul Rehman1, Omar Alharbi2, Yazeed Qasaymeh2

  • 1Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan.

Sensors (Basel, Switzerland)
|March 17, 2025
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概括

本研究介绍了DC-NFC,这是一个深度学习框架,用于在物联网中确保近场通信 (NFC). 它增强了对威胁的安全性和隐私性,实现了高精度和低延迟,以提供强大的保护.

关键词:
物联网一切一切一切一切一切深度学习是一种深度学习.接近现场的通信.保护隐私 保护隐私 保护隐私安全的安全的安全的安全的安全.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 近场通信 (NFC) 对物联网 (IoT) 生态系统至关重要,它可以实现基于近距离的数据交换.
  • 支持NFC的物联网设备面临着重大的安全和隐私风险,包括窃听,中继攻击和欺骗.

研究的目的:

  • 引入DC-NFC,这是一种新的深度学习框架,用于在物联网环境中增强NFC安全性和隐私.
  • 解决无处不在互联网 (IoET) 背景下NFC通信固有的漏洞.

主要方法:

  • DC-NFC框架整合了三个组件:用于模式捕获的上下文编码器 (CE),用于端到端隐私的隐私保护模块 (PML) 和用于实时检测的自适应威胁过器 (ATF).
  • 在四个基准数据集上进行了实验:UNSW-NB15,Bot-IoT,TON-IoT遥测和Edge-IIoTset.

主要成果:

  • 拟议的框架显示了所有测试数据集的安全指标的显著改进.
  • 在UNSW-NB15数据集上实现了高达95%的精度提升,并且在0.90.0以上的F1得分上保持一致.
  • 突出了低延迟的实时处理能力 (在UNSW-NB15上为1000个设备提供20.53秒).

结论:

  • 在各种物联网环境中,DC-NFC提供了一个强大的解决方案,用于增强NFC安全性和隐私.
  • 该框架在威胁检测和实时适应方面的有效性确保了对NFC通信的可靠保护.