The wireless physical-layer identification (WPLI) techniques utilize the unique features of the physical waveforms of wireless signals to identify and classify authorized devices. As the inherent physical-layer features are difficult to forge, WPLI is deemed as a promising technique for wireless security solutions. However, as of today, it still remains unclear whether the existing WPLI techniques can be applied under real-world requirements and constraints. In this paper, through both theoretical modeling and experiment validation, the reliability and the differentiability of the WPLI techniques are rigorously evaluated, especially under the constraints of the state-of-the-art wireless devices, real operation environments, as well as wireless protocols and regulations. In particular, a theoretical model is first established to systematically describe the complete procedure of the WPLI. More importantly, the proposed model is then implemented to thoroughly characterize the various WPLI techniques that utilize the spectrum features coming from the nonlinear RF front-end, under the influences from different transmitters, receivers, and wireless channels. Subsequently, the limitations of the existing WPLI techniques are revealed and evaluated in details using both the developed theoretical model and the in-lab experiments. The real-world requirements and constraints are characterized along each step in WPLI, including: 1) the signal processing at the transmitter (device to be identified); 2) the various physical-layer features that originate from circuits, antenna, and environments; 3) the signal propagation in various wireless channels; 4) the signal reception and processing at the receiver (the identifier); and 5) the fingerprint extraction and classification at the receiver.

With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in autonomous vehicles (AVs). In addition, mmWave sensing has superior reliability compared to other sensing counterparts such as camera and LiDAR, which is essential for safety-critical driving. Therefore, it is critical to understand the security vulnerabilities and improve the security and reliability of mmWave sensing in AVs. To this end, we perform the end-to-end security analysis of a mmWave-based sensing system in AVs, by designing and implementing practical physical layer attack and defense strategies in a state-of-the-art mmWave testbed and an AV testbed in real-world settings. Various strategies are developed to take control of the victim AV by spoofing its mmWave sensing module, including adding fake obstacles at arbitrary locations and faking the locations of existing obstacles. Five real-world attack scenarios are constructed to spoof the victim AV and force it to make dangerous driving decisions leading to a fatal crash. Field experiments are conducted to study the impact of the various attack scenarios using a Lincoln MKZ-based AV testbed, which validate that the attacker can indeed assume control of the victim AV to compromise its security and safety. To defend the attacks, we design and implement a challenge-response authentication scheme and a RF fingerprinting scheme to reliably detect aforementioned spoofing attacks.

Extremely large-scale multiple-input multiple-output (XL-MIMO) promises to provide ultrahigh data rates in millimeter-wave (mmWave) and Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition and beamforming. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose two low-overhead hierarchical beam training schemes for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Then we point out three critical criteria for XL-MIMO hierarchical beam training. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Thirdly, given the imperfect coverage and overlapping of spatial-chirp beams, we further design an enhanced hierarchical training codebook via manifold optimization and alternative minimization. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

For small, inexpensive, and power-constrained IoT devices, Radiofrequency fingerprinting (RF-fingerprinting) has emerged as a cost-effective security solution. Robustness and permanence of the RF-fingerprints (RFFs) are major challenges since this solution’s inception. This is due to domain-related complications such as environmental effects and time-varying device-related perturbations. Since data from domains have divergent distributions, blindly plugging in Machine learning algorithms can overfit domain-related residuals rather than the fingerprint. Recent popular methods like blind channel equalization-based solutions only partially solve this problem while adversely affecting the RFF’s user capacity. Our paper presents a solution to overcome the domain generalization of these computationally intensive feature mining methods in a real-world wireless domain while retaining the fingerprints’ richness. We perform a reverse analysis of a typical RFIC and create a parametric RF-impairment distribution model currently missing in the literature. Then, we use this model to tailor a knowledge-based parametric signal processing and conditioning method, which would create an optimum signal representation of the RFF for ML algorithms. Additionally, our method can significantly reduce the dimensionality of the data needed to train the ML algorithms, eliminate noise, and simplify the classifier needed for RF-fingerprinting. We present our results after evaluation using real-world cross-domain experiments under varying domain conditions with COTS IoT microchips (SX1276).

The wireless communications in complex environments, such as underground and underwater, can enable various applications in the environmental, industrial, homeland security, law enforcement, and military fields. However, conventional electromagnetic wave-based techniques do not work due to the lossy media and complicated structures. Magnetic induction (MI) has been proved to achieve reliable communication in such environments. However, due to the small antenna size, the communication range of MI is still very limited, especially for the portable mobile devices. To this end, Metamaterial-enhanced MI (M ² I) communication has been proposed, where the theoretical results predict that it can significantly increase the data rate and range. Nevertheless, there exists a significant gap between the theoretical prediction and the practical realization of M ² I; the theoretical model relies on an ideal spherical metamaterial, while it does not exist in nature. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M ² I communication. The full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M ² I communication, the communication range can be significantly extended, exactly as we predicted in the ideal M ² I model. Finally, the proposed M ² I communication is implemented and tested in various environments.

In Internet of Things, where billions of devices with limited resources are communicating with each other, security has become a major stumbling block affecting the progress of this technology. Existing authentication schemes based on digital signatures have overhead costs associated with them in terms of computation time, battery power, bandwidth, memory, and related hardware costs. Radio frequency fingerprint (RFF), utilizing the unique device-based information, can be a promising solution for IoT. However, traditional RFFs have become obsolete because of low reliability and reduced user capability. Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead. The injection of fingerprint is implemented using a low cost metasurface developed and fabricated in our lab, which is designed to make small but detectable perturbations in the specific frequency band in which the IoT devices are communicating. We have conducted comprehensive system evaluations including distance, orientation, multiple channels where the feasibility, effectiveness, and reliability of these fingerprints are validated. The proposed MeRFFI system can be easily integrated into the existing authentication schemes. The security vulnerabilities are analyzed for some of the most threatening wireless physical layer-based attacks.