Journal Paper
Journal Paper
Hinge: An Environment-Varying Adaptive Physical-Layer Key Generation Scheme
IEEE Transactions on Information Forensics and Security (TIFS)
Zihao Yang; Lin Wang, Yawen Zheng, Fan Dang, Xikai Sun, Zijuan Liu, Yunhao Liu
On low-power, low-cost Internet of Things (IoT) edges, coarse-grained entropy source-based physical-layer key generation (PKG) is often used, which results in a very low bit generation rate (BGR). In this paper, a novel PKG scheme, Hinge, designed to adapt to varying environmental conditions is introduced to optimize the trade-off between the bit mismatch rate (BMR) and BGR using fine-grained entropy sources on IoT devices. Hinge predicts channel reciprocity levels from one side and dynamically adjusts the quantization strategy, maintaining a low BMR while maximizing BGR. Compared with existing PKG solutions on Bluetooth devices, Hinge yields significant improvements in BGR, with a comparable BMR. Through extensive experiments, Hinge showcases its potential for providing a secure and efficient key generation mechanism for IoT devices in complex real-world scenarios.
Bluekey: Exploiting bluetooth low energy for enhanced physical-layer key generation
IEEE Transactions on Mobile Computing (TMC)
Yawen Zheng, Fan Dang, Zihao Yang, Jinyan Jiang, Xu Wang, Lin Wang, Kebin Liu, Xinlei Chen, Yunhao Liu
Bluetooth Low Energy (BLE) is a prevalent technology in various applications due to its low power consumption and wide device compatibility. Despite its numerous advantages, the encryption methods of BLE often expose devices to potential attacks. To fortify security, we investigate the application of Physical-layer Key Generation (PKG), a promising technology that enables devices to generate a shared secret key from their shared physical environment. Although extensively investigated, PKG is generally discussed in the context of Wi-Fi, and existing solutions for BLE demonstrate significantly lower performance. To bridge this gap, we propose a distinctive approach that capitalizes on the inherent characteristics of BLE to facilitate efficient PKG. We utilize the constant tone extension within BLE protocols to extract comprehensive physical layer information and introduce an innovative method that employs Legendre polynomial quantization for PKG. This method facilitates the exchange of secret keys with a high key matching rate and a high key generation rate. The efficacy of our approach is validated through extensive experiments on a software-defined radio platform, underscoring its potential to enhance security in the rapidly expanding field of BLE applications. A pilot study on commercial off-the-shelf BLE devices further validates the system’s practicality, revealing important trade-offs between performance and hardware constraints in real-world deployments.
Ear2Pos: A Dual-IMU Framework for Full-Body Pose Estimation Using Earbuds
IEEE Internet of Things Journal (IOTJ)
Haolong Wang, Zihao Yang, Hao Wang, Lin Wang
In this article, we present Ear2Pos, a novel framework for full-body pose estimation using only two inertial measurement units (IMUs) integrated into earbuds. Unlike traditional motion capture systems requiring multiple sensors, Ear2Pos leverages a minimal setup to achieve high-accuracy 3-D motion reconstruction. The system incorporates a dual-coordinate framework and Transformer-based modeling to predict joint positions and rotations. Additionally, we propose a personalized skeletal parameterization mechanism, utilizing extracted bone lengths from a single image to enhance individual adaptability. Extensive evaluations demonstrate that Ear2Pos achieves state-of-the-art accuracy in pose estimation when using two sensors, outperforming other methods in upper body motion prediction with an average joint position error of 5.04 cm. Furthermore, we explore clinical applications, particularly in gait analysis for cervical spondylotic myelopathy (CSM) patients, showcasing the framework’s potential for rehabilitation assessment. These findings indicate that Ear2Pos is a promising lightweight solution for noninvasive motion capture, offering robust performance in both research and real-world applications.
Conference Paper
Bluekey: Exploiting bluetooth low energy for enhanced physical-layer key generation
IEEE INFOCOM 2024-IEEE Conference on Computer Communications
Yawen Zheng, Fan Dang, Zihao Yang, Jinyan Jiang, Xu Wang, Lin Wang, Kebin Liu, Xinlei Chen, Yunhao Liu
Bluetooth Low Energy (BLE) is a prevalent technology in various applications due to its low power consumption and wide device compatibility. Despite its numerous advantages, the encryption methods of BLE often expose devices to potential attacks. To fortify security, we investigate the application of Physical-layer Key Generation (PKG), a promising technology that enables devices to generate a shared secret key from their shared physical environment. We propose a distinctive approach that capitalizes on the inherent characteristics of BLE to facilitate efficient PKG. We harness the constant tone extension within BLE protocols to extract comprehensive physical layer information and introduce an innovative method that employs Legendre polynomial quantization for PKG. This method facilitates the exchange of secret keys with a high key matching rate and a high key generation rate. The efficacy of our approach is validated through extensive experiments on a software-defined radio platform, underscoring its potential to enhance security in the rapidly expanding field of BLE applications.
6Loda: Pattern Filtering and Ensemble Learning for IPv6 Target Generation and Scanning
IEEE INFOCOM 2025-IEEE Conference on Computer Communications
Xikai Sun, Fan Dang, Zihao Yang, Xinqi Jin, Junhao Li, Yunhao Liu
IPv6 target generation is crucial for surveying the vast IPv6 address space, which is essential for network management and IPv6 deployment policies. However, existing techniques often suffer from low hit rates due to ineffective space partitioning caused by outlier addresses and limitations in current outlier removal algorithms. To address these challenges, we propose 6Loda, a novel approach that combines pattern filtering and ensemble learning to efficiently remove outlier addresses and discover active IPv6 addresses. Given a set of known active addresses, 6Loda first employs a pattern-based filter to preliminarily eliminate some outlier addresses. It then utilizes a two-level (divisive hierarchical clustering) DHC algorithm to partition the seed set and applies the Loda algorithm to automatically remove remaining outliers in address spaces. Finally, 6Loda implements the random generation algorithm to produce addresses with high hit rates. Experiments conducted on large-scale datasets demonstrate that 6Loda achieves a × 2.26 improvement in hit rate compared to state-of-the-art methods, while maintaining the same budget constraints.