Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Elmaghbub, Abdurrahman Hamdaoui, Bechir |
| author_facet | Elmaghbub, Abdurrahman Hamdaoui, Bechir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security Elmaghbub, Abdurrahman Hamdaoui, Bechir Cryptography and Security Networking and Internet Architecture Signal Processing 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. |
| title | Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security |
| topic | Cryptography and Security Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2402.05332 |