Despite the proven capabilities of deep neural networks (DNNs) in identifying devices through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep learning models have been largely overlooked. While the threat of backdoor attacks is well-studied in the image domain, few works have explored this threat in the context of RF signals. In this paper, we thoroughly analyze the susceptibility of DNN-based RF fingerprinting to backdoor attacks, focusing on a more practical scenario where attackers lack access to control model gradients and training processes. We propose leveraging explainable machine learning techniques and autoencoders to guide the selection of trigger positions and values, allowing for the creation of effective backdoor triggers in a model-agnostic manner. To comprehensively evaluate this backdoor attack, we employ four diverse datasets with two protocols (Wi-Fi and LoRa) across various DNN architectures. Given that RF signals are often transformed into the frequency or time-frequency domains, this study also assesses attack efficacy in the time-frequency domain. Furthermore, we experiment with potential detection and defense methods, demonstrating the difficulty of fully safeguarding against our proposed backdoor attack. Additionally, we consider the attack performance in the domain shift case.

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings.

In this paper, we propose a novel approach to cross-domain unmanned aerial vehicle (UAV) authentication using radio frequency (RF) fingerprinting based on prototypical networks (PTNs). UAVs present a unique challenge for RF fingerprinting due to their hovering motion, which creates more diverse signal domains compared to other RF devices like Wi-Fi. This results in a severe domain shift problem, where well-trained models struggle to generalize to unseen domains. To address this issue without incurring significant costs in data collection and model retraining, we employ PTNs, a few-shot learning paradigm that enhances cross-domain performance and system viability. We further improve our method's effectiveness by incorporating fine-tuning with data augmentation, maintaining system viability while improving performance. Comprehensive experimental results demonstrate that our approach significantly mitigates domain shift, achieving up to a 20% improvement in cross-domain accuracy for UAV fingerprinting.

Palm recognition has emerged as a dominant biometric authentication technology in critical infrastructure. These systems utilize palm-related biometric features, including palmprint and palmvein data, either individually in a single-modal setting or jointly in a dual-modal. Despite the different forms, they all employ similar hardware architectures that inadvertently emit electromagnetic (EM) signals during operation. Our research reveals that these EM emissions leak palm biometric information, motivating us to develop EMPalm—an attack framework that covertly recovers both palmprint and palmvein images from eavesdropped EM signals. Specifically, we first separate the interleaved transmissions of the visible (palmprint) and NIR (palmvein) modalities, identify the informative frequency bands of each modality, and then combine these bands to reconstruct the corresponding images. To overcome the strong noise and distortions inherent in side-channel acquisition, we further employ a diffusion model to restore fine-grained biometric features. Evaluations on seven prototype and three commercial palm acquisition devices show that EMPalm can recover biometric information from real human palms with high visual fidelity, achieving Structural Similarity Index Measure (SSIM) scores up to 0.79, Peak Signal-to-Noise Ratio (PSNR) up to 29.88 dB, and Fréchet Inception Distance (FID) scores as low as 6.82 across all tested devices. Compared with the best state-of-the-art method, which can only reconstruct palm-vein images, EMPalm improves overall reconstruction fidelity by 33% and uniquely supports high-quality recovery for both palmprint and palm-vein modalities. To assess the practical implications of the attack, we further evaluate the recovered palm images against four state-of-the-art palm recognition models through real-time experiments, achieving a model-wise average spoofing success rate of 65.30%.