In recent years, WiFi-based gesture recognition (WGR) has gained popularity due to its privacy-preserving nature and the wide availability of WiFi infrastructure. However, existing WGR systems suffer from scalability issues, i.e., requiring extensive data collection and re-training for each new gesture class. To address these limitations, we propose OneSense, a one-shot WiFi-based gesture recognition system that can efficiently and easily adapt to new gesture classes. Specifically, we first propose a data enrichment approach based on the law of signal propagation in physical world to generate virtual gestures, enhancing the diversity of the training set without extra overhead of real sample collection. Then, we devise an aug-meta learning (AML) framework to enable efficient and scalable few-short learning. This framework leverages two pre-training stages (i.e., aug-training and meta-training) to improve the model’s feature extraction and generalization abilities, and ultimately achieves accurate one-shot gesture recognition through fine-tuning. Experimental results demonstrate that OneSense achieves 93% one-shot gesture recognition accuracy, which outperforms the state-of-the-art approaches. Moreover, it maintains high recognition accuracy when facing new environments, user locations, and user orientations. Furthermore, the proposed AML framework reduces 86%+ pre-training latency compared to conventional meta-learning method.

Optical camera communication (OCC) holds potential for location-aware data transfer, facilitating applications such as localization and overlaying digital content for mixed reality experiences. However, existing OCC designs commonly require a clean background for reliable demodulation, rendering its use disruptive and impractical. To this end, we propose WinkLink, a novel OCC system capable of robust transmission behind complex backgrounds, even under low signal-to-noise ratio (SNR) conditions. We address the key challenge of extracting subtle signals in the lossy OCC channel by designing a two-stage deep neural network and a context-aware demodulation protocol. The proposed system is trained solely on a synthesized dataset yet generalizes effectively to unseen real-world backgrounds. Through experiments in 12 diverse environments, we demonstrate that WinkLink successfully transmits OCC signals under a low SNR of -20 dB, achieving a substantial 5.8 dB SNR gain. This low SNR translates to an extended distance to 5.5× of baseline (11m with a 10W LED transmitter) and negligible interference on concurrent vision applications. Finally, WinkLink proves its efficacy even when the device is moving, i.e., dynamic backgrounds, making it ready for deployment on mobile devices.

GPU cryptojacking is an attack that hijacks GPU resources of victims for cryptocurrency mining. Such attack is becoming an emerging threat to both local hosts and cloud platforms. These attacks result in huge economic losses for the victims due to significant power consumption by cryptomining applications. Unfortunately, there are no adequate solutions to detect such attacks. In this paper, we propose MagTracer, a novel GPU cryptojacking detection system that leverages magnetic leakage signals emanating from GPUs. We make a key observation that GPUs emanate a distinct magnetic signal while mining, which can be attributed to the core feature of all cryptomining algorithms (as they are compute-intensive as well as memory-bounded). We design and implement a proof-of-concept detection system to demonstrate MagTracer's feasibility. We evaluate MagTracer on 14 heterogeneous GPU models and achieve a high average true positive rate of over 98% and a low false positive rate below 0.7% in all cases. Furthermore, our comprehensive evaluation confirms that MagTracer is scalable across different mining applications and robust against several targeted attacks.

WiFi-based Human Gesture Recognition (HGR) becomes increasingly promising for device-free human-computer interaction. However, existing WiFi-based approaches have not been ready for real-world deployment due to the limited scalability, especially for unseen gestures. The reason behind is that when introducing unseen gestures, prior works have to collect a large number of samples and re-train the model. While the recent advance of few-shot learning has brought new opportunities to solve this problem, the overhead has not been effectively reduced. This is because these methods still require enormous data to learn adequate prior knowledge, and their complicated training process intensifies the regular training cost. In this paper, we propose a WiFi-based HGR system, namely OneFi, which can recognize unseen gestures with only one (or few) labeled samples. OneFi fundamentally addresses the challenge of high overhead. On the one hand, OneFi utilizes a virtual gesture generation mechanism such that the massive efforts in prior works can be significantly alleviated in the data collection process. On the other hand, OneFi employs a lightweight one-shot learning framework based on transductive fine-tuning to eliminate model re-training. We additionally design a self-attention based backbone, termed as WiFi Transformer, to minimize the training cost of the proposed framework. We establish a real-world testbed using commodity WiFi devices and perform extensive experiments over it. The evaluation results show that OneFi can recognize unseen gestures with the accuracy of 84.2, 94.2, 95.8, and 98.8% when 1, 3, 5, 7 labeled samples are available, respectively, while the overall training process takes less than two minutes.