Radio Frequency-Enhanced Multi-Factor IoT Device Authentication via Swarm Learning

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-06 DOI:10.1109/TNSE.2025.3548813

Fanqin Zhou;Lei Zhang;Zhixiang Yang;Lei Feng

引用次数: 0

Abstract

With the increased popularity of Internet of things (IoT) devices, security issues have notably risen in recent times. Typically, wireless IoT applications are vulnerable to impersonation attacks by malicious entities. This paper proposes a lightweight multi-factor authentication mechanism boosted by radio frequency fingerprinting (RFF) to physically identify IoT devices. A novel application of swarm learning (SL) is utilized to develop the authentication model and enable distributed authentication. This approach maintains privacy and is resilient against faults when processing RFF data from various sources. The device-side multi-factor authentication is lightweight and has been validated through a formal security model. Experimental results indicate that the proposed scheme achieves the highest authentication success rate and the lowest computational cost on the device side compared to other authentication methods, which also validated its effectiveness in defending against impersonation and poisoning attacks.

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基于群学习的射频增强多因素物联网设备认证

随着物联网（IoT）设备的日益普及，近年来安全问题明显增加。通常，无线物联网应用程序容易受到恶意实体的模拟攻击。本文提出了一种由射频指纹（RFF）增强的轻量级多因素身份验证机制，用于物理识别物联网设备。提出了一种新的应用群学习的方法来建立认证模型，实现分布式认证。这种方法维护了隐私，并且在处理来自各种来源的RFF数据时具有抗故障的弹性。设备端多因素身份验证是轻量级的，并且已经通过正式的安全模型进行了验证。实验结果表明，与其他认证方法相比，该方案在设备端获得了最高的认证成功率和最低的计算成本，验证了该方案在防御冒充和投毒攻击方面的有效性。

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

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.