Browsing by Author "Li, Jiawei"
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Item Rhythmic RFID Authentication(IEEE/ACM Transactions on Networking, 2022-09-14) Li, Jiawei; Wang, Chuyu; Li, Ang; Han, Dianqi; Zhang, Yan; Zuo, Jinhang; Zhang, Rui; Xie, Lei; Zhang, YanchaoPassive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user’s tapping rhythm. In addition to verifying the RFID card’s identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user’s secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.Item SpecKriging: GNN-based Secure Cooperative Spectrum Sensing(IEEE Transactions on Wireless Communications, 2022-06-14) Zhang, Yan; Li, Ang; Li, Jiawei; Dianqi, Han; Li, Tao; Zhang, Rui; Zhang, YanchaoCooperative spectrum sensing (CSS) adopted by spectrum-sensing providers (SSPs) plays a key role for dynamic spectrum access and is essential for avoiding interference with licensed primary users (PUs). A typical SSP system consists of geographically distributed spectrum sensors which can be compromised to submit fake spectrum-sensing reports. In this paper, we propose SpecKriging, a new spatial-interpolation technique based on Inductive Graph Neural Network Kriging (IGNNK) for secure CSS. In SpecKriging, we first pretrain a graphical neural network (GNN) model with the historical sensing records of a few trusted anchor sensors. During system runtime, we use the trained model to evaluate the trustworthiness of non-anchor sensors’ data and also use them along with anchor sensors’ new data to retrain the model. SpecKriging outputs trustworthy sensor reports for spectrum-occupancy detection. To the best of our knowledge, SpecKriging is the first work that explores GNNs for trustworthy CSS and also incorporates the hardware heterogeneity of spectrum sensors. Extensive experiments confirm the high efficacy and efficiency of SpecKriging for trustworthy spectrum-occupancy detection even when malicious spectrum sensors constitute the majority.