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Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security
IEEE Wireless Communications ( IF 12.9 ) Pub Date : 2024-04-10 , DOI: 10.1109/mwc.001.2300420
Abdurrahman Elmaghbub 1 , Bechir Hamdaoui 1
Affiliation  

Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. This interconnectivity raises concerns about privacy and security, given the potential network-wide impact of a single compromise. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework designed to serve as the device authentication layer within the ZT architecture, with a focus on resource-constrained IoT devices. At the core of EPS-CNN, a Convolutional Neural Network (CNN) is utilized to generate the device identity from a unique RF signal representation, known as the Double-Sided Envelope Power Spectrum (EPS), which effectively captures the device-specific hardware characteristics while ignoring device-unrelated information. Experimental evaluations show that the proposed framework achieves over 99%, 93%, and 95% of testing accuracy when tested in same-domain (day, location, and channel), crossday, and cross-location scenarios, respectively. Our findings demonstrate the superiority of the proposed framework in enhancing the accuracy, robustness, and adaptability of deep learning-based methods, thus offering a pioneering solution for enabling ZT IoT device identification.

中文翻译:

用于零信任物联网安全的基于域无关硬件指纹的设备标识符

下一代网络的目标是全面连接,将人、机器、设备和系统无缝互连。考虑到单一妥协可能对整个网络产生影响,这种互连性引发了人们对隐私和安全的担忧。为了应对这一挑战,零信任(ZT)范式应运而生,成为保护网络完整性和数据机密性的关键方法。这项工作介绍了 EPS-CNN,这是一种基于深度学习的新型无线设备识别框架,旨在充当 ZT 架构中的设备身份验证层,重点关注资源受限的物联网设备。 EPS-CNN 的核心是利用卷积神经网络 (CNN) 从独特的 RF 信号表示(称为双面包络功率谱 (EPS))生成设备身份,该信号可有效捕获设备特定的硬件特征,同时忽略与设备无关的信息。实验评估表明,该框架在同域(日、位置和通道)、跨日和跨位置场景下测试时分别实现了超过 99%、93% 和 95% 的测试准确率。我们的研究结果证明了所提出的框架在增强基于深度学习的方法的准确性、鲁棒性和适应性方面的优越性,从而为实现 ZT IoT 设备识别提供了开创性的解决方案。
更新日期:2024-04-10
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