Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913930675224576 |
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| author | Al-Hazbi, Saeif Hussain, Ahmed Sciancalepore, Savio Oligeri, Gabriele Papadimitratos, Panos |
| author_facet | Al-Hazbi, Saeif Hussain, Ahmed Sciancalepore, Savio Oligeri, Gabriele Papadimitratos, Panos |
| contents | Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges, originating from the embedded systems and the DL research domains. This paper systematically identifies and analyzes the essential considerations and challenges encountered in the creation of DL-based RFF systems across their typical development life-cycle, which include (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation provides a comprehensive overview of the current open problems that prevent real deployment of DL-based RFF systems while also discussing promising research opportunities to enhance the overall accuracy, robustness, and privacy of these systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16406 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities Al-Hazbi, Saeif Hussain, Ahmed Sciancalepore, Savio Oligeri, Gabriele Papadimitratos, Panos Cryptography and Security Artificial Intelligence Signal Processing Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges, originating from the embedded systems and the DL research domains. This paper systematically identifies and analyzes the essential considerations and challenges encountered in the creation of DL-based RFF systems across their typical development life-cycle, which include (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation provides a comprehensive overview of the current open problems that prevent real deployment of DL-based RFF systems while also discussing promising research opportunities to enhance the overall accuracy, robustness, and privacy of these systems. |
| title | Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities |
| topic | Cryptography and Security Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2310.16406 |