Radar spoofing attacks mislead victim radars by injecting false information. Successful attacks require prior knowledge of the victim radar's mode and parameters, and existing works obtain this critical information with expensive equipment, e.g., software-defined radio or spectrum analyzer. In this paper, we proposeWaston, a low-cost system for radar mode detection and parameter estimation using commercial off-the-shelf (COTS) mmWave radars. To overcome the disadvantage of low sampling frequency of COTS mmWave radars, we design two special local signals to detect frequency points and spectral shapes for radar mode detection. We propose a novel parameter estimation algorithm to estimate frequency- and time-domain parameters for spoofing different radars. We have implemented a prototype on the TI AWR1843 platform and conducted extensive experiments to evaluate the performance ofWaston. Our experimental results demonstrate thatWastonachieves an accuracy of 100$\%$for mode detection and 99$\%$for parameter estimation. Furthermore, we demonstrate that the estimated parameters can be used to launch a successful spoofing attack against the victim radar.

Wireless-based human activity recognition (WHAR) enables various promising applications. However, since WHAR is sensitive to changes in sensing conditions (e.g., different environments, users, and new activities), trained models often do not work well under new conditions. Recent research uses meta-learning to adapt models. However, they must fine-tune the model, which greatly hinders the widespread adoption of WHAR in practice because model fine-tuning is difficult to automate and requires deep-learning expertise. The fundamental reason for model fine-tuning in existing works is because their goal is to find the mapping relationship between data samples and corresponding activity labels. Since this mapping reflects the intrinsic properties of data in the perceptual scene, it is naturally related to the conditions under which the activity is sensed. To address this problem, we exploit the principle that under the same sensing condition, data of the same activity class are more similar (in a certain latent space) than data of other classes, and this property holds invariant across different conditions. Our main observation is that meta-learning can actually also transform WHAR design into a learning problem that is always under similar conditions, thus decoupling the dependence on sensing conditions. With this capability, general and accurate WHAR can be achieved, avoiding model fine-tuning. In this paper, we implement this idea through two innovative designs in a system called RoMF. Extensive experiments using FMCW, Wi-Fi and acoustic three sensing signals show that it can achieve up to 95.3% accuracy in unseen conditions, including new environments, users and activity classes.

We propose a practical and efficient radar interference mitigation system, Radar-CA. Radar-CA overcomes the limitations of requiring any additional equipment or resources. Radar-CA transfers the access time estimation problem to a frequency estimation problem, enabling interference mitigation through a central controller, and our preliminary result shows that Radar-CA is capable of mitigating interference efficiently in a dense radar network.

Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues. For example, a user’s daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and locate such spy radars. In this paper, we proposeRadar2, a practical system for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novelFrequency Component Detectionmethod to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector’s observations at multiple anchor points. Not only doesRadar2work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we performed extensive experiments to evaluate the effectiveness and robustness ofRadar2in various settings. Our evaluation results show that the radar detection rate is above 96% and the localization error is within 0.3m. The results also reveal thatRadar2is robust against various environmental factors (e.g., room layout and human activities).