Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (“fingerprint”) the device itself based on variations introduced in the transmitted waveform. Key impediments in developing robust and scalable RFFDL techniques that are practical in dynamic and mobile environments are the non-stationary behavior of the wireless channel and other impairments introduced by the propagation conditions. To date, the existing RFFDL-based techniques have only been able to demonstrate a desirable performance when the training and testing environment remains the same, which makes the solutions impractical.SignCRFbrings to the RFFDL landscape what it has been missing so far: a scalable, channel-agnostic data-driven radio authentication platform with unmatched precision in fingerprinting wireless devices based on their unique manufacturing impairments that isindependent of the dynamic nature of the environment or channel irregularities caused by mobility.SignCRFconsists of: (i) a classifier developed in a base-environment with minimum channel dynamics, and finely trained to authenticate devices with high accuracy and at scale; (ii) an environment translator that is carefully designed and trained to remove the dynamic channel impact from RF signals while maintaining the radio's specific “signature”; and (iii) a Max Rule module that selects the highest precision authentication technique between the baseline classifier and the environment translator per radio. We design, train, and validate the performance ofSignCRFfor multiple technologies in dynamic environments and at scale (100 LoRa and 20 WiFi devices, the largest datasets available in the literature). We assess the scalability ofSignCRFacross various testbed scales by validating our system using small, medium, and large-scale testbeds, with sizes of 5, 20, and 100 devices, respectively. We demonstrate thatSignCRFcan significantly improve the RFFDL performance by achieving as high as 100% correct authentication for WiFi devices and 80% correctly authenticated LoRa devices, a 5x and 8x improvement when compared to the state-of-the-art respectively. Furthermore, we show thatSignCRFis resilient to adversarial actions by reducing the device recognition accuracy from 73% to 6%, which translates into zero mis-authentication of adversary radios that try to impersonate legitimate devices, which has not been achieved by any prior RFFDL techniques.

The Long Range (LoRa) protocol for low-power wide-area networks (LPWANs) is a strong candidate to enable the massive roll-out of the Internet of Things (IoT) because of its low cost, impressive sensitivity (-137dBm), and massive scalability potential. As tens of thousands of tiny LoRa devices are deployed over large geographic areas, a key component to the success of LoRa will be the development of reliable and robust authentication mechanisms. To this end, Radio Frequency Fingerprinting (RFFP) through deep learning (DL) has been heralded as an effective zero-power supplement or alternative to energy-hungry cryptography. Existing work on LoRa RFFP has mostly focused on small-scale testbeds and low-dimensional learning techniques; however, many challenges remain. Key among them are authentication techniques robust to a wide variety of channel variations over time and supporting a vast population of devices.

Deep learning can be used to classify waveform characteristics (e.g., modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has demonstrated that one of the most crucial challenges in wireless deep learning is to counteract the channel action, which may significantly alter the waveform features. The problem is further exacerbated by the fact that deep learning algorithms are hardly re-trainable in real time due to their sheer size. This paper proposesDeepFIR, a framework to counteract the channel action in wireless deep learning algorithmswithout retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter’s side, we can apply tiny modifications to the waveform to strengthen its features according to the current channel conditions. We mathematically formulate theWaveform Optimization Problem(WOP)as the problem of finding the optimum FIR to be used on a waveform to improve the classifier’s accuracy. We also propose a data-driven methodology to train the FIRs directly with dataset inputs. We extensively evaluateDeepFIRon an experimental testbed of 20 software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices and a 24-class modulation dataset. Experimental results show that our approach (i) increases the accuracy of the radio fingerprinting models by about 35%, 50% and 58%; (ii) decreases an adversary’s accuracy by about 54% when trying to imitate other device’s fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset; (iv) increases by$2\times$the accuracy of the modulation dataset.

Radio fingerprinting uniquely identifies wireless devices by leveraging tiny hardware-level imperfections inevitably present in off-the-shelf radio circuitry. This way, devices can be directly identified at the physical layer by analyzing the unprocessed received waveform - thus avoiding energy-expensive upper-layer cryptography that resource-challenged embedded devices may not be able to afford. Recent advances have proven that convolutional neural networks (CNNs) - thanks to their multidimensional mappings - can achieve fingerprinting accuracy levels impossible to achieve by traditional low-dimensional algorithms. The same research, however, has also suggested that the wireless channel may negatively impact the accuracy of CNN-based radio fingerprinting algorithms by making device-unique hardware imperfections much harder to recognize.In spite of the growing interest in radio fingerprinting research by academia and DARPA, the wireless research community still lacks (i) a large-scale open dataset for radio fingerprinting collected in diverse environments and rich, diverse, channel conditions; and (ii) a full-fledged, systematic, quantitative investigation of the impact of the wireless channel on the accuracy of CNN-based radio fingerprinting algorithms. The key contribution of this paper is to bridge this gap by (i) collecting and sharing with the community more than 7TB of wireless data obtained from 20 wireless devices with identical RF circuitry (and thus, worst-case scenario for fingerprinting) over the course of several days in (a) an anechoic chamber, (b) in-the-wild testbed, and (c) with cable connections; and (ii) providing a first-of-its-kind evaluation of the impact of the wireless channel on CNN-based fingerprinting algorithms through (a) the 7TB experimental dataset and (b) a 400GB dataset provided by DARPA containing hundreds of thousands of transmissions from thousands of WiFi and ADS-B devices with different SNR conditions. Experimental results conclude that (i) the wireless channel impacts the classification accuracy significantly, i.e., from 85% to 9% and from 30% to 17% in the experimental and DARPA dataset, respectively; and that (ii) equalizing I/Q data can increase the accuracy to a significant extent (i.e., by up to 23%) when the number of devices increases significantly.

Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by leveraging the unique hardware-level imperfections imposed on the received wireless signal by the transmitter's radio circuitry. Most of existing approaches utilize hand-tailored protocol-specific feature extraction techniques, which can identify devices operating under a pre-defined wireless protocol only. Conversely, by mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard.