Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security

• URL: http://arxiv.org/abs/2402.05332v1
• Date: Thu, 8 Feb 2024 00:23:42 GMT
• Title: Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security
• Authors: Abdurrahman Elmaghbub, Bechir Hamdaoui,
• Abstract summary: Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. 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.
• Score: 7.8344795632171325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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% testing accuracy when tested in same-domain (day, location, and channel), cross-day, 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.

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