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基于多任务原型学习的开放式无线电频率指纹识别方法

Zhao Ma¹, Shengliang Fang¹, Youchen Fan¹

¹School of Aerospace Information, Space Engineering University, Beijing 101400, China.

Sensors (Basel, Switzerland)

|September 13, 2025

概括

本研究介绍了一种使用多任务原型学习 (MTPL) 的开放式射频指纹识别 (RFFI) 方法,以增强物联网 (IoT) 安全性. 这种新的方法有效地识别未知设备,改进了无线身份验证系统.

科学领域:

网络安全网络安全
信号处理信号处理
机器学习机器学习

背景情况:

无线电频率 (RF) 指纹对物联网 (IoT) 安全至关重要.
现有的方法在现实场景中经常失败,原因是"封闭式"假设,无法检测新型设备.
对于动态的物联网环境,需要强大的开放式识别方法.

研究的目的:

提出一个开放式的无线电频率指纹识别 (RFFI) 方法.
解决当前射频指纹技术中"封闭集"假设的局限性.
提高物联网中的无线身份验证系统的安全性和稳定性.

主要方法:

开发了一个多任务原型学习 (MTPL) 框架,集成了一个编码器,解码器和分类器.
在深度网络中使用了歧视性分类,生成性重建和原型集群任务.
利用极端价值理论 (EVT) 建立基于原型距离的自适应式开放式歧视值.

主要成果:

拟议的MTPL方法在16个Wi-Fi设备的真实数据集上实现了0.9918的平均AUROC.
超过了五种主流的开放式识别方法,包括SoftMax值,OpenMax和MLOSR.
在识别已知和未知设备方面表现出卓越的性能,增强无线身份验证安全性.

结论:

新的MTPL方法有效地解决了RF指纹识别中的开放式识别挑战.
集成EVT为适应性值提供了一个强大的机制.
该方法显著提高了物联网无线认证系统的安全性和可靠性.

关键词:

无线电指纹识别 (RFFI) 是一个功能.极端价值理论 (EVT) 是一种极端价值理论.这是一个开放式设置.原型学习学习的原型学习.

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