No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments

Friday, May 3, 2019
Speaker(s) : Kaushik Chowdhury (Northeastern University, USA)

Abstract: Due to the unprecedented scale of the Internet of Things, designing scalable, accurate, energy-efficient and tamper-proof authentication mechanisms has now become more important than ever. To this end, in this paper we present ORACLE, a novel system based on convolutional neural networks (CNNs) to ``fingerprint’’, i.e., identify a unique radio from a large pool of devices by deep-learning the fine-grained hardware impairments imposed by radio circuitry on physical-layer I/Q samples. First, we show how hardware-specific imperfections are learned by the CNN framework. Then, we extensively evaluate the performance of ORACLE on several first-of-its-kind large-scale datasets of WiFi- transmissions collected ``in the wild'', as well as a dataset of nominally-identical (i.e., equal baseband signals) WiFi devices, reaching 80-90% accuracy is many cases with the error gap arising due to channel-induced effects. Finally, we show through an experimental testbed, how this accuracy can reach over 99% by intentionally inserting and learning the effect of controlled impairments at the transmitter side, to completely remove the impact of the wireless channel. Furthermore, to scale this approach for classifying potential thousands of radios, we propose an impairment hopping spread spectrum (IHOP) technique that is resilient to spoofing attacks. In summary, ORACLE makes two important contributions: (i) It demonstrates on actual radios how to overcome wireless channel effects and achieve the `train once deploy anywhere' paradigm that is missing so far in RF-fingerprinting methods today. (ii) It proposes CNN architectures for classifying very large radio population of up to 500 devices.
Bio: Prof. Kaushik R. Chowdhury received the PhD degree from the Georgia Institute of Technology, Atlanta, in 2009. He is currently Associate Professor and Faculty Fellow in the Electrical and Computer Engineering Department at Northeastern University, Boston, MA. He was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) in Jan. 2017 by President Obama, the DARPA Young Faculty Award in 2017, the Office of Naval Research Director of Research Early Career Award in 2016, and the NSF CAREER award in 2015. He received multiple best paper awards, including the IEEE INFOCOM conference in 2018, ICC conference, in 2009, ’12 and ’13, and ICNC conference in 2013. His works have gathered over 9700 citations. His current research interests include machine learning for radios, networking for unmanned aerial systems, wireless RF energy harvesting and IoT and in the area of intra/on-body communication. He is a co-director for the Platforms for Advanced Wireless Research project office, a joint $100 million public-private partnership between the US National Science Foundation and a wireless industry consortium to create city-scale testing platforms.

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