arXiv subjects

Shuangrui Zhao

Publications and source records attributed to Shuangrui Zhao.

5 recordsLinked to original sources

arXiv2026

C$^2$T-OpenMax: A Novel Open-Set WiFi RF Fingerprinting Method via Center Constrained Learning and Confidence-Guided Tail Modeling

Radio frequency fingerprinting (RFF) enables device authentication from transmitter-specific hardware imperfections, but practical deployment requires cross-environment open-set recognition. Data augmentation improves environmental generalization, yet may yield dispersed, low-confidence known-class representations that distort the class statistics used by OpenMax. To address this problem, we propose C$^2$T-OpenMax, an enhanced OpenMax framework combining center-constrained learning with confidence-guided tail modeling. The former improves intra-class compactness, making class-wise representations more suitable for distance-based modeling. The latter retains only correctly classified, high-confidence logits for mean activation vector estimation and Weibull fitting, reducing bias from ambiguous boundary samples. Together, the two modules refine representation geometry and OpenMax construction while preserving augmentation benefits. Experiments on a public WiFi CSI dataset show that C$^2$T-OpenMax achieves the highest open-set accuracy in seven of eight location groups and outperforms all baselines in area under the receiver operating characteristic curve (AUROC) and open-set classification rate (OSCR) across every tested openness level. Under the largest-openness setting, it improves accuracy by 12.31%, AUROC by 0.0887, and OSCR by 0.0856 over the augmented OpenMax baseline.

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arXiv2026

MARL-Based Sequential RIS Auctions: A Physical-Layer Security Analysis

Reconfigurable intelligent surfaces (RISs) hold great potential to enhance coverage, spectral efficiency, and communication security by intelligently configuring their reflecting elements. When owned by a neutral RIS operator, these elements can be offered as resources for which legitimate receivers and eavesdroppers compete. This paper investigates such competition and evaluates its impact on the physical-layer security performance of legitimate receivers. To model the competition, we develop a sequential RIS auction (SRA) framework, in which a bundle of RIS elements is auctioned in each round through a first-price sealed-bid mechanism, with each bidder submitting its bid based on the achievable rate gain and remaining budget. We then formulate the sequential bidding process as a Markov game by specifying its states, actions, rewards, and state transitions. To solve the game, we propose a multi-bidder deep deterministic policy gradient (MADDPG)-based multi-bidder reinforcement learning (MARL) approach under centralized training and decentralized execution (CTDE), enabling legitimate receivers and eavesdroppers to learn bidding strategies that maximize their long-term economic surplus. Numerical results show that, under the considered eavesdropper bidding strategies, the RL-based strategy enables legitimate receivers to achieve the highest secrecy rate per unit cost, outperforming random and fixed strategies and approaching the ideal physical-layer upper bound.

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arXiv2026

IriSig-Spoof: A Real-World Benchmark for Time-Robust Satellite RF Fingerprinting and Spoofing Detection

Low Earth orbit (LEO) satellite Internet is becoming critical communications infrastructure, yet its open wireless links remain vulnerable to satellite impersonation and signal spoofing. Radio frequency fingerprinting (RFF) offers a potential defense by exploiting transmitter-specific hardware imperfections manifested in received signals. However, the reliability of existing satellite RFF methods remains difficult to assess because no unified dataset and benchmark support temporal, open-set, and cross-scenario evaluation. To address this gap, we introduce IriSig-Spoof, a real-world Iridium dataset comprising 5.17 million messages collected from 66 satellites over 32 days, together with software-defined radio (SDR)-generated spoofing signals from indoor and outdoor settings. We further establish three benchmark tasks: temporal robustness evaluation, open-set RFF identification with unknown-signal rejection, and cross-scenario spoofing detection. Experiments using a multi-scale attention convolutional neural network (MACNN) show that temporal robustness varies across configurations, with the best configuration achieving 97.75% average cross-day accuracy. In open-set evaluation, MACNN achieves an area under the receiver operating characteristic curve (AUROC) of 0.9715, while showing that effective unknown-signal rejection does not necessarily ensure reliable identity assignment. Cross-scenario experiments reveal differences at low false-positive rates. IriSig-Spoof provides a reproducible basis for evaluating robust RFF methods under temporal variation and changing attack conditions.

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arXiv2026

On a General Theoretical Framework for Radio Frequency Fingerprint-Based Authentication

While radio frequency fingerprint (RFF)-based wireless device authentication has been widely studied across different datasets and scenarios, there still lacks a fundamental theory to explain why and how RFF can serve as a reliable device identity, significantly hindering the practical application of such an authentication technology. In this article, we integrate the RFF modeling with authentication property analysis to propose a general theoretical framework to facilitate the development of such a theory. The RFF modeling process reveals how RFFs are induced, evolved and observed along the transmitter-channel-receiver chain, built upon which, the authentication property analysis process then outlines how the trustworthiness of an RFF should be examined in terms of its uniqueness, stability, distinguishability, and unforgeability. By linking the RFF formation/evolution to these authentication properties, the framework offers a solid foundation for understanding why and how RFF-based authentication is trustworthy in practice. We also discuss the communication-authentication co-design issue based on the theoretical insights from the proposed framework.

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arXiv2022

Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless Networks

While the millimeter-wave (mmWave) communication is robust against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an eavesdropper can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.

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