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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
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.
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.
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