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Amani Al-shawabka’s PhD Proposal Review
January 31, 2023 @ 1:00 pm - 2:00 pm
“Channel-and-Adversary-Resilient Radio Fingerprinting through Data-Driven Approaches at Scale”
Radio fingerprinting authenticates wireless devices by leveraging tiny hardware-level imperfections inevitably present in the radio circuitry. This way, devices can be directly identified at the physical layer– thus avoiding energy-expensive upper-layer cryptography that resource-limited embedded devices may not be able to afford. Recent advances have proven that employing deep learning algorithms can achieve fingerprinting accuracy levels that were impossible to achieve by traditional low-dimensional algorithms. Still, the wireless research community lacks an exhaustive understanding of the challenges associated with developing robust, reliable, and channel-resilient radio fingerprinting through deep-learning approaches for practical applications. Key challenges are the non-stationarity of the wireless channel, and the dynamic effects introduced by the operational environment, which significantly limit fingerprinting applications by obscuring the hardware impairments associated with the transmitted waveform.
In this thesis, we (i) develop a full-fledged, systematic investigation to quantify the impact of the wireless channel by providing a first-of-its-kind evaluation on deep-learning-based fingerprinting algorithms, examining the worst-case scenario (employing devices with identical radio circuitry) and at scale; (ii) develop large-scale open datasets for radio fingerprinting collected in diverse, rich, channel conditions and environments, and using different technologies, including WiFi and LoRa; (iii) identify conditions where hardware impairments are still detectable; and (iv) design, implement, and benchmark new data-driven algorithms to counter the degradation introduced by the wireless channel. Notably, we propose a generalized, real-time channel- and adversary-resilient data-driven approach to authenticate wireless devices at scale in practical scenarios. To the best of our knowledge, our work for the first time improves the fingerprinting accuracy of the worst-case scenario with up to 4x and 6.3x for WiFi and LoRa technologies, respectively.
Prof. Tommaso Melodia (Advisor)
Prof. Kaushik Chowdhury
Prof. Francesco Restuccia