Searcharxiv⌕ Search

arXiv subjects

Yulong Shen

Publications and source records attributed to Yulong Shen.

At least 19 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.

cs.CR↗
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.

cs.CR↗
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.

cs.CR↗
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.

cs.CR↗
arXiv2026

HoRFFI: High-Openness RF Fingerprint Identification with a Similarity-Enhanced Variational Information Bottleneck

Radio frequency fingerprint identification (RFFI) is a promising technique for wireless device authentication. However, practical RFFI systems must enroll newly authorized devices while rejecting previously unseen ones, even when the feature extractor is trained on only a few labeled base-device classes, giving rise to a high-openness RFFI problem. Existing open-set recognition methods typically rely on feature spaces learned from a large and diverse set of known-device classes, limiting their applicability in practical scenarios. To address this challenge, we propose HoRFFI, a high-openness RFFI framework that supports scalable device identification and unknown-device rejection using only a small number of labeled training devices. HoRFFI employs a similarity-enhanced variational information bottleneck (SVIB)-based supervision mechanism, which reduces the encoder's dependence on training-class diversity and learns a more transferable embedding space. This supervision mechanism uses feature-space augmentation and clustering to derive inter-sample similarity information, which provides supplementary supervision for regularizing the embedding space. Experiments on public LoRa and Wi-Fi datasets show that HoRFFI achieves absolute improvements of \(0.112\) and \(0.288\) in novel-class accuracy, respectively, and corresponding absolute AUC improvements of \(0.029\) and \(0.060\) over the best-performing baselines.

cs.CR↗
arXiv2026

FedGSA: Geometry-Consistent Subspace Aggregation for Differentially Private Federated LoRA

Low-Rank Adaptation (LoRA) enables communication-efficient federated fine-tuning of pretrained language models. However, integrating differential privacy (DP) into federated LoRA remains challenging: independently perturbing and aggregating its two low-rank matrices can cause aggregation mismatch and the quadratic noise term. Existing methods mitigate these issues by freezing one low-rank matrix but still rely on Euclidean aggregation, which is basis-dependent and may distort the global update. To address this limitation, we propose FedGSA, a geometry-consistent aggregation framework for differentially private federated LoRA. FedGSA represents each privatized client update as a basis-invariant subspace on the Grassmann manifold. In each communication round, clients extract low-dimensional subspaces capturing dominant update directions and encode them as projection matrices. The server aggregates these representations to estimate a geometry-consistent global update subspace and reconstructs the global LoRA factors within it, reducing distortion caused by basis misalignment, privacy noise, and heterogeneous client updates. We prove that FedGSA incurs no additional privacy loss beyond client-side DP training and establish its convergence under standard assumptions. Experiments on four GLUE tasks and a language generation benchmark demonstrate consistent improvements across privacy budgets and degrees of data heterogeneity. In particular, FedGSA improves average accuracy over the strongest baseline by 2.17% and 2.27% under $\epsilon=6$ and $\epsilon=3$, respectively.

cs.CR↗
arXiv2026

Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models

Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evaluations. Existing aggregation-based DP-ZO methods reconstruct model updates at a fixed scale, ignoring that the strength of useful signals varies throughout training. Consequently, noise-dominated updates may receive excessive weight and degrade model utility. To address this issue, we propose SAGE, a noise-aware shrinkage method that adaptively attenuates privatized estimates according to their estimated signal quality. SAGE subtracts the known Gaussian noise variance from the observed second moment to estimate the underlying signal energy, stabilizes this estimate through temporal tracking, and compares its current signal-to-noise level with a warm-up reference to derive a bounded shrinkage factor. As pure post-processing, SAGE requires neither additional privacy budget nor model queries and introduces only constant additional state. Our theoretical analysis shows that shrinkage reduces the quadratic update-risk term faster than the linear descent term, preserving useful descent while limiting the influence of noise-dominated updates. Experiments on RoBERTa-large, OPT-1.3B, and OPT-6.7B demonstrate that SAGE outperforms existing baselines in most settings under the same privacy budgets while preserving the forward-only memory efficiency of DP-ZO.

cs.LG↗
arXiv2026

GLST: Defending Confidence-Driven V2X Collaborative Perception Against Stealthy Multi-Attacker Feature Injection

Collaborative perception (CP) improves autonomous-driving perception by enabling connected vehicles to exchange intermediate features via V2X. Confidence-driven sparse communication reduces bandwidth by transmitting only perception-critical spatial regions, but creates a security risk: once a collaborator is compromised, malicious features in high-confidence or ego-uncertain regions may be preferentially selected and amplified during fusion. Using Where2comm as a representative framework, we show that the proposed Pretend Benign attack exploits its spatial-confidence mechanism by injecting stealthy perturbations into uncertain yet perception-critical regions, substantially degrading 3D object detection while preserving benign-like feature characteristics. Beyond this attack-framework pair, we identify a broader weakness of existing trust-based defenses: their reliance primarily on a single consistency signal leaves them vulnerable when multiple attackers form a pseudo-consensus that biases trust estimation. We therefore propose Global-Local Structural Trust (GLST), a lightweight defense that assesses collaborator reliability through three complementary perspectives: global feature consistency, multi-scale local residual consistency, and structural consistency with ego-side semantic topology. The resulting trust scores guide feature fusion to suppress unreliable collaborators. Experiments on OPV2V show that GLST achieves competitive performance against single-attacker Pretend Benign attacks and substantially stronger robustness in multi-attacker settings. Under a four-attacker Pretend Benign attack, GLST maintains 0.69 AP@0.3, whereas existing single-signal defenses degrade severely. GLST also remains effective against gradient-based attacks such as PGD, indicating that multi-level trust modeling is essential for securing confidence-driven CP.

cs.CR↗
arXiv2026

RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control

Natural-language control offers a promising interface for unmanned aerial vehicles (UAVs), but directly applying self-hosted computer-use agents (SHCUAs) to UAV control introduces a structural mismatch. SHCUAs are designed for interactive host-side tool use, where delayed agent iterations are often acceptable. UAV control, however, is coupled with continuously changing physical states, strict timing constraints, safety risks, and security accountability. A stale, unauthorized, or tampered agent decision may therefore lead to unsafe or untraceable vehicle behavior. This paper proposes a real-time and security-oriented restructuring of SHCUA-based UAV control. Instead of allowing an SHCUA to directly issue flight commands, we transform its outputs into contract-bound UAV skill invocations with explicit timing, state, authority, fallback, and evidence semantics. Based on this abstraction, we design an architecture that separates semantic reasoning from onboard execution and security/safety enforcement. Slow cloud or edge reasoning is used for mission understanding, while onboard components validate and dispatch only timely, authorized, and state-consistent skills. Security-critical enforcement points can be protected by TEE-style or microcontroller isolation mechanisms without moving the full language agent or high-frequency flight-control loop into trusted components. Prototype evaluation shows that RT-SHCUA maintains bounded task-level responsiveness while supporting degraded handling, trusted admission, and auditable evidence preservation for SHCUA-mediated UAV actions.

cs.CR↗
arXiv2026

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems

Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents. However, existing tools exhibit limited effectiveness in real-time anomaly detection due to the complex dependencies among sensors and actuators. To tackle this, we present IstGPT, the first industrial anomaly detection tool based on LLMs and graph learning to provide real-time protection against a wide range of ICS attacks. IstGPT achieves fine-grained and precise modeling on spatial-temporal dependencies in industrial cyber-physical systems. It first leverages industrial multi-modal knowledge, including operational data, technical documents, and system diagrams, to extract sensor-actuator dependency graphs via multi-stage prompt engineering. Then, LLM-Optimation iteratively refines the graph based on node accuracy, edge consistency, and logical coherence. Finally, IstGPT integrated improved graph neural networks with an encoder-decoder architecture to detect anomalies via reconstruction errors. We evaluate IstGPT against 12 state-of-the-art baselines on 9 datasets, including 2 public, 6 simulated, and a real-world robotic arm dataset. IstGPT achieves the best F1-scores and eTaF1 (a newer time-aware metric) across nine datasets. We further discuss the feasibility of deploying IstGPT in real-world industrial scenarios.

cs.CR↗
arXiv2026

EBCC: Enclave-Backed Confidential Containers via OCI-Compatible Runtime Integration

Container runtimes provide a stable operational interface for deploying, monitoring, and controlling modern workloads, while trusted execution environments (TEEs) provide hardware-enforced isolation for sensitive computation. Existing confidential-container systems often rely on VM-backed deployment stacks or TEE-specific execution substrates, which can separate confidential execution from the conventional OCI runtime lifecycle. This paper presents EBCC (Enclave-Backed Confidential Containers), an OCI-compatible runtime architecture for managing composite confidential-computing workloads. EBCC treats the REE-side anchor and TEE-side confidential stages as a single containerized confidential-computing composite, preserves standard OCI lifecycle operations, and keeps TEE-specific execution behind a backend adapter. It also maintains persistent per-instance state and per-stage artifacts for request handling, response generation, logging, and evidence binding. We implement EBCC on a Keystone backend and evaluate its correctness, performance, footprint, and concurrent execution behavior. The results show that EBCC introduces additional latency over native Keystone execution, mainly due to lifecycle mediation, request validation, EID allocation, backend dispatch, and artifact persistence, while keeping the added footprint concentrated on host-side management state. Cross-TEE case studies on SGX, TDX, and OP-TEE show that the same lifecycle and stage abstraction can be mapped to enclave-style, VM-style, and embedded-style TEEs. These results indicate that EBCC can make TEE-backed execution manageable through an OCI-style lifecycle without materially enlarging the protected-side TCB.

cs.CR↗
arXiv2026

Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation

Self-hosted computer-use agents (SHCUAs), such as OpenClaw, combine natural-language interaction with direct access to host-side resources, including browsers, files, scripts, system commands, and external communication channels. While useful for automating real tasks, this capability also creates a host-level abuse surface: a legitimately deployed agent may be steered toward unsafe operations through malicious messages, indirect prompt injection, unsafe skills, or tampering along the host-side control path. We argue that such risks cannot be addressed by ad hoc blocking rules alone, because the security criticality of an operation depends jointly on its action type, target object, execution context, and potential effect. This paper presents an operation-centric model for risk-based confinement of SHCUA operations. The proposed design keeps ordinary functionality on the constrained REE path, while protecting security-critical classification, authorization, binding, evidence generation, and selected execution-control decisions inside a cloud-native TEE-backed trusted operation plane. We instantiate the architecture on OpenClaw using Intel TDX as the primary trusted backend, with remote terminal-side trusted components verifying TDX-audited commands before constrained local execution. The evaluation shows that the design can block unsafe or policy-disallowed operations before execution, preserve ordinary functionality for allowed workloads, and provide auditable evidence with deployment-dependent overhead.

cs.CR↗
arXiv2026

When Convenience Becomes Risk: A Semantic View of Under-Specification in Host-Acting Agents

Host-acting agents promise a convenient interaction model in which users specify goals and the system determines how to realize them. We argue that this convenience introduces a distinct security problem: semantic under-specification in goal specification. User instructions are typically goal-oriented, yet they often leave process constraints, safety boundaries, persistence, and exposure insufficiently specified. As a result, the agent must complete missing execution semantics before acting, and this completion can produce risky host-side plans even when the user-stated goal is benign. In this paper, we develop a semantic threat model, present a taxonomy of semantic-induced risky completion patterns, and study the phenomenon through an OpenClaw-centered case study and execution-trace analysis. We further derive defense design principles for making execution boundaries explicit and constraining risky completion. These findings suggest that securing host-acting agents requires governing not only which actions are allowed at execution time, but also how goal-only instructions are translated into executable plans.

cs.CR↗
arXiv2026

Quantum criticality in sub-Ohmic systems with three competing terms: beyond conventional spin-boson physics

Quantum phase transitions (QPTs) in the spin-boson model with/without the rotating-wave approximation (RWA) are systematically investigated through variational calculations using a sub-Ohmic bath with high spectral density. Four cases involving different system-environment interactions are examined, where transition points and critical exponents are accurately determined across varying tunneling strengths. Contrary to prior work, a rich phase diagram is revealed in the tunneling-coupling plane even at the low spectral exponent $s<1/2$, with a novel U(1)-symmetric phase being identified. As coupling increases, a multi-stage QPT sequence arises for the tunneling $0<Δ< Δ^*=0.074(1)$, whereas a single transition occurs beyond this range. Furthermore, an odd-parity phase is found to emerge even under the positive tunneling, exhibiting distinct characteristics relative to the prototype model.

quant-ph↗
arXiv2026

On Achievable Covert Communication Performance under CSI Estimation Error and Feedback Delay

Covert communication's effectiveness critically depends on precise channel state information (CSI). This paper investigates the impact of imperfect CSI on achievable covert communication performance in a two-hop relay system. Firstly, we introduce a two-hop covert transmission scheme utilizing channel inversion power control (CIPC) to manage opportunistic interference, eliminating the receiver's self-interference. Given that CSI estimation error (CEE) and feedback delay (FD) are the two primary factors leading to imperfect CSI, we construct a comprehensive theoretical model to accurately characterize their effects on CSI quality. With the aid of this model, we then derive closed-form solutions for detection error probability (DEP) and covert rate (CR), establishing an analytical framework to delineate the inherent relationship between CEE, FD, and covert performance. Furthermore, to mitigate the adverse effects of imperfect CSI on achievable covert performance, we investigate the joint optimization of channel inversion power and data symbol length to maximize CR under DEP constraints and propose an iterative alternating algorithm to solve the bi-dimensional non-convex optimization problem. Finally, extensive experimental results validate our theoretical framework and illustrate the impact of imperfect CSI on achievable covert performance.

cs.IT↗
arXiv2026

Optimal Beamforming for Uplink Covert Communication in MIMO GEO Satellite-Terrestrial Systems

This paper investigates the uplink covert communication in a multiple-input multiple-output (MIMO) satellite-terrestrial system consisting of an Earth station transmitter Alice, a geosynchronous Earth orbit (GEO) satellite receiver Bob, and multiple GEO satellite wardens around Bob, where each node in the system is equipped with an array of directional antennas. Based on beamforming and the default antenna orientation setting, we first propose a scheme for covert Alice-Bob uplink transmission. Under the perfect channel estimation scenario, we provide theoretical modeling for the system performance in terms of detection error probability (DEP), transmission outage probability (TOP) and covert rate (CR), and then explore the optimal beamforming (OB) design as well as the joint optimal beamforming and antenna orientation (JO-BA) design for CR maximization. We then extend our study to the imperfect channel estimation scenario, and conduct related performance modeling and OB/JO-BA designs for CR maximization. We also apply the techniques of semidefinite relaxation, alternating optimization, Rodrigues' rotation formula and 1-D search algorithm to develop efficient algorithms to solve the above optimization problems. Finally, extensive numerical results are presented to verify our theoretical results and to illustrate the efficiency of beamforming and antenna orientation design for supporting the uplink covert communication in MIMO GEO satellite-terrestrial systems.

cs.IT↗
arXiv2026

Enhancing LLM-based Specification Generation via Program Slicing and Logical Deletion

Traditional formal specification generation methods are typically tailored to specific specification types, and therefore suffer from limited generality. In recent years, large language model (LLM)-based specification generation approaches have emerged, offering a new direction for improving the universality of automated specification synthesis. However, when dealing with complex control flow, LLMs often struggle to precisely generate complete specifications that cover substructures. Moreover, the distinctive verification pipelines adopted by existing approaches may incorrectly discard logically correct specifications, while verification tools alone cannot reliably identify correct specifications. To address these issues, we propose SLD-Spec, an LLM-based specification generation method that combines program slicing and logical deletion. Specifically, SLD-Spec augments the conventional specification generation framework with two key stages: (1) a program slicing stage that decomposes the target function into several smaller code slices, enabling LLMs to focus on more localized semantic structures and thereby improving specification relevance and completeness; and (2) a logical deletion stage that leverages LLMs to perform logical reasoning and filtering over candidate specifications so as to retain logically correct ones. Experimental results show that SLD-Spec consistently outperforms existing methods on datasets containing programs of varying complexity, verifying more programs and generating specifications that are more relevant and more complete. Further ablation studies indicate that program slicing mainly improves specification relevance and completeness, whereas logical deletion plays a key role in increasing verification success rates.

cs.SE↗
arXiv2026

Beyond Basic Specifications? A Systematic Study of Logical Constructs in LLM-based Specification Generation

Formal specifications play a pivotal role in accurately characterizing program behaviors and ensuring software correctness. In recent years, leveraging large language models (LLMs) for the automatic generation of program specifications has emerged as a promising avenue for enhancing verification efficiency. However, existing research has been predominantly confined to generating specifications based on basic syntactic constructs, falling short of meeting the demands for high-level abstraction in complex program verification. Consequently, we propose incorporating logical constructs into existing LLM-based specification generation framework. Nevertheless, there remains a lack of systematic investigation into whether LLMs can effectively generate such complex constructs. To this end, we conduct an empirical study aimed at exploring the impact of various types of syntactic constructs on specification generation framework. Specifically, we define four syntactic configurations with varying levels of abstraction and perform extensive evaluations on mainstream program verification datasets, employing a diverse set of representative LLMs. Experimental results first confirm that LLMs are capable of generating valid logical constructs. Further analysis reveals that the synergistic use of logical constructs and basic syntactic constructs leads to improvements in both verification capability and robustness, without significantly increasing verification overhead. Additionally, we uncover the distinct advantages of two refinement paradigms. To the best of our knowledge, this is the first systematic work exploring the feasibility of utilizing LLMs for generating high-level logical constructs, providing an empirical basis and guidance for the future construction of automated program verification framework with enhanced abstraction capabilities.

cs.SE↗