physical-layer security

In multi-modal wireless networks (WiFi/LoRa/Cellular/Zigbee), covert communication faces challenges including multiple colluding eavesdroppers, uncertain channel-state statistics, and stringent full-covertness requirements. Method: This paper proposes a joint routing and multi-interface link configuration framework for covert communication. It introduces the first polynomial-time optimal algorithm that jointly optimizes coverage and covertness, integrating combinatorial optimization, covert communication modeling, and stochastic robust optimization to enable robust decision-making under multiple adversaries. Contribution/Results: The framework provides theoretical guarantees on maximizing end-to-end covert throughput while strictly bounding the adversary’s detection probability. Numerical experiments demonstrate a 2.3× improvement in covert throughput over single-modal baselines and sustain >98% detection-avoidance probability under multi-adversary settings.

This work addresses the dual security challenges in integrated sensing and communication (ISAC) systems, where a shared waveform simultaneously supports secure communication and environmental sensing, yet remains vulnerable to both eavesdropping and sensing privacy leakage. The paper establishes the first unified physical-layer security framework that explicitly characterizes the intrinsic coupling between communication secrecy and sensing privacy. It proposes a joint security mechanism integrating feedback-based key extraction, eavesdropper-channel coding, and resolvability-based coding. By optimizing the joint input distribution, the study reveals the fundamental trade-offs among secrecy rate, legitimate sensing performance, and adversarial sensing suppression capability, derives the achievable secure performance region, and validates the proposed approach through numerical experiments, thereby laying a theoretical foundation for secure ISAC system design.

This study addresses the fundamental trade-off between reliability and covertness in wireless covert communications under channel and noise uncertainty. Focusing on quasi-static fading scenarios, it investigates reliability characterized by outage probability and covertness evaluated via radiometer detection. By modeling uncertainties with bounded sets and employing a conditional large-N mid-point threshold radiometer proxy, the work reveals that reliability and covertness are governed by distinct worst-case conditions. Building on this insight, a conflict-aware robust design framework is proposed, yielding closed-form expressions for the robust feasible transmit power and optimal transmission rate. Numerical results demonstrate that uncertainty substantially shrinks the system’s feasible region and degrades performance, while the proposed proxy model achieves high accuracy at low effective signal-to-noise ratios.

This work addresses the vulnerability of physical-layer communications to side-channel attacks, which can lead to data theft, eavesdropping, and denial-of-service, as traditional protocols often fail to ensure the confidentiality and integrity of data types and destinations. To counter these threats, the paper proposes a novel physical-layer security protocol framework that integrates data-flow awareness with integrity verification mechanisms. By synergistically combining side-channel analysis, anomaly behavior detection, and physical-layer security techniques, the framework effectively mitigates man-in-the-middle and advanced persistent threats targeting copper, fiber-optic, and wireless media. Experimental results demonstrate that the proposed approach significantly enhances data confidentiality and resilience against interference at the physical layer, while substantially reducing the success rate of advanced persistent physical attacks.

To address the asymmetry between attacker and defender inherent in physical-layer security (PLS)’s passive defense paradigm, this paper proposes a Physical-Layer Deception (PLD) framework. PLD actively injects semantically consistent deceptive messages into eavesdroppers’ observations via a two-stage encoding scheme that integrates randomized cipher coding with non-orthogonal multiple access (NOMA). Crucially, PLD guarantees information-theoretic ciphertext confidentiality under the weak assumption that the legitimate channel quality exceeds that of the eavesdropper—even when the eavesdropper possesses identical prior knowledge as the legitimate receiver. We formally prove PLD’s security and validate its superiority through numerical simulations, demonstrating significant gains in secrecy rate and robustness against eavesdropping compared to conventional PLS schemes. This work constitutes the first systematic introduction of active deception mechanisms into physical-layer security, thereby relaxing the stringent channel-condition requirements imposed by classical PLS approaches.

This study investigates the detrimental impact of artificial noise elimination (ANE) on the physical-layer security performance of artificial noise (AN) schemes, with a focus on the sustainability of secrecy rates in multi-antenna eavesdropping channels. Employing information-theoretic methods, the work establishes, for the first time, scaling laws for both average and instantaneous secrecy rates to quantitatively characterize the erosion of AN’s security gains due to ANE. The key contribution lies in uncovering a critical relationship among the numbers of antennas at the transmitter, legitimate receiver, and eavesdropper: secure communication may fail when the eavesdropper’s antenna count exceeds twice that of the transmitter. Furthermore, the paper provides a sufficient condition under which AN remains effective despite ANE. These findings offer theoretical foundations and practical design guidelines for robust physical-layer security systems resilient to ANE attacks.

This study addresses a critical security gap in public safety communication systems—such as TETRA, TETRAPOL, and P25—whose signaling planes have long transmitted metadata in plaintext, even when voice payloads are encrypted. By leveraging software-defined radio (SDR) for passive eavesdropping, combined with protocol reverse engineering and metadata correlation analysis, this work demonstrates for the first time that nationwide network mapping and user tracking are feasible using signaling data alone. The research uncovers a standards-level confidentiality flaw in TETRAPOL’s emergency call mechanism and successfully recovers key operational parameters, including base station and terminal identities, group mobility patterns, key domain boundaries, and rotation cycles. Notably, it also extracts unencrypted voice content from TETRAPOL transmissions, underscoring fundamental weaknesses in the signaling confidentiality design of current public safety networks.

This work addresses the security challenges in LLM-enabled edge networks, where frequent wireless interactions and electromagnetic emissions render systems vulnerable to eavesdropping, interference, and prompt injection attacks, while existing defenses incur prohibitive overhead. To overcome this limitation, the paper introduces— for the first time—a lightweight security framework that synergistically integrates covert communication principles with computation. By jointly incorporating electromagnetic leakage suppression, low-overhead encryption, and secure scheduling strategies, the proposed approach simultaneously preserves the privacy of LLM tasks and significantly enhances execution efficiency. Experimental results demonstrate that under stringent security constraints, the method effectively reduces overall latency, achieving a co-optimized balance between security and performance and thereby transcending conventional high-overhead protection paradigms.

This work addresses the security and privacy challenges arising from the deep integration of communication, sensing, and computing in next-generation wireless networks. It presents the first systematic unification of security considerations across these three domains, proposing an end-to-end, cross-layer cooperative security framework. By integrating cutting-edge techniques—including physical-layer security, semantic/pragmatic communication security, privacy-preserving sensing, and secure coded computation—the framework establishes a comprehensive defense mechanism spanning the entire data pipeline. The study not only identifies novel attack surfaces and risk mechanisms inherent to such integrated architectures but also provides theoretical foundations and practical pathways to enhance data privacy, authentication, and overall system robustness.

This work addresses the reliance on traditional cryptographic assumptions by proposing a wiretap coding scheme within a semantic communication framework that leverages the intrinsic randomness of wireless channels to jointly ensure security and reliable transmission. The design employs mutual information (MI) and generalized mutual information (GMI) as optimization criteria for two canonical eavesdropping scenarios, respectively, and integrates maximum a posteriori (MAP) decoding with deep learning–driven discrete semantic representations. By uniquely unifying semantic communication, information-theoretic metrics, and deep learning for wiretap code construction, this approach significantly reduces information leakage to eavesdroppers of varying capabilities while maintaining high reliability for the legitimate receiver.