FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Han Wang, David Eklund, Alina Oprea, S. Raza
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引用次数: 1

Abstract

Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∼99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy.
FL4IoT:使用联邦学习的物联网设备指纹识别和识别
网络中未识别的设备可能会导致毁灭性的后果。因此,有必要对连接到专用或关键网络的物联网设备进行指纹识别和识别。随着大量异构物联网设备的激增,检测连接到网络的易受攻击设备变得越来越具有挑战性。当前基于机器学习的指纹识别和设备识别技术需要从物联网网络收集大量数据,这些数据必须传输到中央云。然而,在许多敏感场景中,私有物联网数据无法与中心云共享。联邦学习(FL)被认为是分散学习的一种很有前途的范例,并已被应用于许多不同的用例中。它使机器学习模型能够以一种保护隐私的方式进行训练。在本文中,我们提出了一种使用FL保护隐私的物联网设备指纹和识别机制;我们称之为FL4IoT。FL4IoT是基于无监督学习的设备指纹识别和基于监督学习的设备识别相结合的两阶段系统。FL4IoT在联邦和集中式设置的不同性能指标中显示了它的实用性。例如,在最好的情况下,经验结果表明,FL4IoT在使用联邦设置识别物联网设备方面达到了~ 99%的准确率和F1-Score,而不会将任何私有数据暴露给集中式云实体。此外,FL4IoT可以检测欺骗设备,准确率超过99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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