在LoRa网络中使用联合学习来检测被改的无线电频率传输的隐私保护检测
Nurettin Selcuk Senol1, Mohamed Baza2, Amar Rasheed1
1Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
联合学习 (FL) 通过允许隐私保护检测改的无线电频率传输和未知攻击来提高LoRa网络的安全性. 卷积式自编码器联合学习 (CAE-FL) 在检测这些异常方面取得了最高的准确性.
科学领域:
- 网络安全 网络安全
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 机器学习 机器学习
背景情况:
- 低功耗,远程物联网通信的LoRa网络至关重要.
- 洛拉的无线电频率传输易受改和攻击.
- 现有的安全方法可能会损害数据隐私.
研究的目的:
- 开发一种保护隐私的方法,用于检测LoRa网络中被改的传输.
- 在基于LoRa的物联网系统中识别未知攻击.
- 利用联合学习 (FL) 来安全地检测异常,而无需共享原始数据.
主要方法:
- 评估了多个支持FL的异常检测算法:CAE-FL,IF-FL,OCSVM-FL,LOF-FL和K-Means-FL.
- 在分布式设备上使用FL训练模型来维护数据隐私.
- 使用准确度,精度,回忆和F1分数来评估性能.
主要成果:
- 卷积式自编码器联合学习 (CAE-FL) 显示出卓越的性能,准确率为97.27%,精度,回忆和F1分数为0.97.
- 隔离森林联合学习 (IF-FL) 实现了96.84%的准确性.
- 基于集群的方法,如OCSVM-FL,LOF-FL和K-Means-FL,显示出强大的检测能力.
结论:
- 联合学习有效地提高了LoRa网络中异常检测的隐私和安全性.
- 拟议的FL方法可以检测被改的信号和未知的攻击,保护敏感的物联网应用程序.
- CAE-FL是一种高效的算法,用于在LoRa通信中保护隐私的异常检测.
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