Natural Language Generation (NLG) applications have gained immense popularity due to the utilization of powerful deep learning techniques and large training corpora. However, the increasing prevalence of NLG models also poses a significant risk of unauthorized access or theft of intellectual property (IP). To safeguard NLG models, watermarking has emerged as a promising tool, but existing watermarking techniques based on pre-processing are prone to attacker detection and can potentially harm NLG applications. This paper proposes a novel, semantic, and stealthy watermarking scheme for IP protection of NLG models. Our approach embeds a semantic combination water mark, which is generated through a multi-stage process designed to be semantic and stealthy. This scheme endows an NLG model with a verifiable preference for specific semantic combinations, which are initiated by a foundational pattern but holistically constructed to preserve model functionality. To enhance the robustness, data embedding is systematically performed through a masked location injection. Consequently, the watermark is seamlessly integrated into NLG models without misleading their original attention mechanism. Comprehensive experiments are conducted to demonstrate that the proposed scheme is highly effective and robust in protecting the IP of NLG models while remaining stealthy to potential attackers.

The security of collaboration among drone swarms necessitates the creation of inter-swarm/group keys. However, current solutions lack an inter-group key establishment mechanism that supports uniformity, flexibility, trustworthiness, efficiency and scalability to enable secure and efficient inter-swarm communications. In this paper, we propose DeGKG, an efficient decentralized inter-group key generation scheme that offers the construction of inter-swarm encryption keys for various drone swarms. It leverages the regional similarity of satellite cluster signals to construct drone swarm public/private key pairs, and employs the Chinese remainder theorem to integrate the swarm public keys for creating inter-group encryption keys, significantly reducing the number of complex cryptographic operations and the burden of inter-swarm key creation, and ensuring the scalability. In addition, the inter-swarm key creation allows the division of drones without the regional similarity of satellite cluster signals into various swarms, each composed of drones with the signal similarity, thus supporting the key establishment among all the drones and efficacy. An efficient blockchain consensus mechanism is implemented to uniformly generate inter-group encryption keys for various swarm combinations without relying on a trusted third party, thus ensuring the efficiency, flexibility, and trustworthiness of the generation. We prove the security of DeGKG, and demonstrate its efficacy and efficiency through simulations and comparisons.

Embodied agents exhibit immense potential across a multitude of domains, making the assurance of their behavioral safety a fundamental prerequisite for their widespread deployment. However, existing research predominantly concentrates on the security of general large language models, lacking specialized methodologies for establishing safety benchmarks and input moderation tailored to embodied agents. To bridge this gap, this paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents. This framework encompasses the entire pipeline, including taxonomy definition, dataset curation, moderator architecture, model training, and rigorous evaluation. Notably, we introduce EAsafetyBench, a meticulously crafted safety benchmark engineered to facilitate both the training and stringent assessment of moderators specifically designed for embodied agents. Furthermore, we propose Pinpoint, an innovative prompt-decoupled input moderation scheme that harnesses a masked attention mechanism to effectively isolate and mitigate the influence of functional prompts on moderation tasks. Extensive experiments conducted on diverse benchmark datasets and models validate the feasibility and efficacy of the proposed approach. The results demonstrate that our methodologies achieve an impressive average detection accuracy of 94.58%, surpassing the performance of existing state-of-the-art techniques, alongside an exceptional moderation processing time of merely 0.002 seconds per instance. The source code and datasets can be found at https://github.com/ZihanYan-CQU/EAsafetyBench.

Leveraging the spatial sensitivity characteristics of millimeter-wave technology to integrate communication and sensing presents a highly promising approach for industrial manufacturing systems. This paper proposes a novel integrated sensing and communication method for detecting the physical spatial location of industrial wireless sensor nodes based on beam pattern trajectories. In particular, to handle the incomplete phenomenon of beam patterns during communication, a beam pattern trajectory fusion method is presented by reconstructing incomplete sector-level sweep (SLS) SNR data to enhance the feature robustness. To address the challenge of small sample size, a Siamese network architecture is designed, leveraging dual-branch feature embeddings to detect sub-centimeter displacements and angular deviations. Experimental evaluations on commercial Talon AD7200 devices achieve over 95% accuracy across diverse distances (1–6 m), hardware variations and temporal conditions, while reducing training data requirements by 50% compared to conventional classifiers. This framework advances Industry 4.0 applications by transforming mmWave communication signals into high-precision, hardware-agnostic displacement sensors, eliminating the need for dedicated instrumentation.

The nonintrusive load monitoring (NILM) technique, through its use of various deep neural networks (DNNs), is capable of learning residential appliances’ usage patterns from networked smart meters. However, such learned information may pose a serious privacy risk to users. In response to this privacy concern, in this article, we introduce an innovative adversarial attack. This attack can effectively restrict the NILM models’ ability to dissect power signals while maintaining accurate electricity charges for users. Given that previous adversarial attacks—which are designed for image classifiers and regressors with one-time output—cannot adequately handle NILM models and regressors with time-series output, we formally present the attack objective by leveraging the unique characteristics of regression and time-series data. Our proposed solution algorithms for this attack objective can generate imperceptible perturbations, effectively misleading the prediction of NILM models. To further ensure accurate billing calculation, we refine the attack objective to a practical version and propose a post-process that can iteratively remove the added perturbation in a certain period without compromising attack effectiveness. Experimental results on two real-world datasets, REDD and UK-DALE, demonstrate the effectiveness, transferability, and practicality of our proposed adversarial attack scheme.

The sixth-generation (6G) mobile communication networks are perceived as large-scale heterogeneous networks. With their increased heterogenization and densification, it is crucial to guarantee the security and efficiency of user equipment's handovers between networks. However, existing cross-domain handover authentication schemes cannot ensure handover authentication efficiency and cannot balance privacy and system efficiency, which thus cannot be directly applied in heterogeneous networks. In this paper, we present PECHA, a privacy-preserving and efficient cross-domain handover authentication scheme for heterogeneous networks, which enables anonymous authentication on user equipment (UE) through the collision property of chameleon hash functions. PECHA ensures authentication efficiency by employing the interplanetary file system and blockchain to synchronize UE's authentication information to target networks in advance. The privacy and system efficiency are balanced by modeling the unlinkability of UE's new and old chameleon hash values and determining the update frequency of UE chameleon hash value. PECHA also achieves correctness, mutual authentication and key agreement, anonymity, unlinkability, conditional privacy, forward/backward secrecy, robustness, known randomness secrecy, key escrow freeness and rapid response, and resists against spoofing attacks, replay attacks and man-in-the-middle attacks. Comprehensive performance analysis, evaluation and comparisons show that PECHA is efficient with respect to both computation and communication.

Reconfigurable Intelligent Surfaces (RIS) can optimize spectrum and energy efficiency in the sixth-generation (6G) wireless communication system through dynamic electromagnetic wave manipulation. The programmable control of spatial electromagnetic signals by RIS presents a double-edged sword, and it can also be exploited by malicious attackers. However, few studies have focused on the detection and identification of such malicious RIS. To fill this gap, we propose a novel spoofing detection framework combining dynamic key-embedded phase codebooks with a dual-channel feature extraction mechanism. This approach jointly decodes wireless channel fingerprints and cryptographic signatures from received signals. A hybrid discriminator, integrating autoencoder-based signal reconstruction fidelity and key-matching validation, enables robust legitimacy verification. The prototype experiments using USRP SDR and RIS hardware show that the verification accuracy of the scheme can reach 100%, when the signal-to-noise ratio (SNR) is above 10dB, the number of training sample points is more than 128, and the codebook dimension is near 32.

Authenticated key agreement (AKA) between vehicles and road side units (RSUs) is crucial in vehicular ad-hoc networks (VANETs). However, existing solutions still suffer from high overheads of AKA and lack a mechanism to balance privacy strength and system efficiency. In this paper, we present a lightweight anonymous authenticated key agreement (LWAKA) scheme for VANETs, supporting lightweight anonymous authentication and key agreement between vehicles and RSUs simultaneously. In particular, vehicles’ authentication information is synchronized to target RSUs in advance for accelerating authentication, and lightweight cryptographic operations (i.e., hash function, hash-based message authentication, physical unclonable function, fuzzy extractor and symmetric encryption) are employed to ensure the high efficiency of AKA in terms of computation and communication overheads. The system efficiency and privacy are balanced through modeling the relationship between the frequency of pseudonym updates and the unlinkability of the vehicles’ new and old pseudonyms. Security analysis shows that LWAKA not only achieves anonymity, conditional privacy, pseudonym unlinkability, key escrow freeness, and physical security, but also resists against most known attacks. Comparative experimental results demonstrate that LWAKA outperforms existing schemes in terms of lightweight design.

Smart grid, through networked smart meters employing the non-intrusive load monitoring (NILM) technique, can considerably discern the usage patterns of residential appliances. However, this technique also incurs privacy leakage. To address this issue, we propose an innovative scheme based on adversarial attack in this paper. The scheme effectively prevents NILM models from violating appliance-level privacy, while also ensuring accurate billing calculation for users. To achieve this objective, we overcome two primary challenges. First, as NILM models fall under the category of time-series regression models, direct application of traditional adversarial attacks designed for classification tasks is not feasible. To tackle this issue, we formulate a novel adversarial attack problem tailored specifically for NILM and providing a theoretical foundation for utilizing the Jacobian of the NILM model to generate imperceptible perturbations. Leveraging the Jacobian, our scheme can produce perturbations, which effectively misleads the signal prediction of NILM models to safeguard users' appliance-level privacy. The second challenge pertains to fundamental utility requirements, where existing adversarial attack schemes struggle to achieve accurate billing calculation for users. To handle this problem, we introduce an additional constraint, mandating that the sum of added perturbations within a billing period must be precisely zero. Experimental validation on real-world power datasets REDD and U.K.-DALE demonstrates the efficacy of our proposed solutions, which can significantly amplify the discrepancy between the output of the targeted NILM model and the actual power signal of appliances, and enable accurate billing at the same time. Additionally, our solutions exhibit transferability, making the generated perturbation signal from one target model applicable to other diverse NILM models.

Spectrum sharing technology is used to alleviate the tension and scarcity of spectrum resources, and federated learning can significantly enhance the performance of tasks such as incumbent detection and improve the quality of spectrum sharing. However, spectrum sharing methods based on federated learning still face challenges such as large-scale data transmission and the lack of privacy protection for sensing nodes. To tackle these issues, in this paper, we propose a compressed sensing (CS) based transmission framework that integrates efficient aggregation and privacy protection. In particular, a multiple measurement vector (MMV)-CS model is used for efficient aggregation between the central server and sensing nodes. By designing different measurement vectors, local environment sensing nodes can be divided into different clusters, forming multiple superimposed transmission signals at the central server. Thus, the central server will obtain the aggregated information of local models from different clusters, completing the optimization of the global model in federated learning. In this process, the efficiency of data aggregation has been greatly improved, and the data privacy of individual environment sensing nodes is protected. The security analysis and simulation results are provided to validate the effectiveness of the proposed schemes. The detection performance of the proposed method is as good as that of the approach under the raw training samples, while the privacy-preserving and communication efficiency are significantly improved.

Group-based content push can be widely applied in integrated networks, where group key management is crucial for the push's security. Existing group key management methods mainly include symmetric group key agreement, broadcast encryption, asymmetric group key agreement, and attribute-based encryption. However, most of them do not consider user equipment (UE) identity privacy and unlinkability, cannot support flexibility and efficiency due to each UE maintaining group keys, and lack the trustworthiness of UE and group key management, which hinders the widespread adoption of group-based content push in trustless environments like integrated networks. In this paper, we investigate a novel decentralized group key management (DeGKM) scheme for group-based content push in integrated networks, where different operators manage pseudonyms and group keys across domains in a decentralized manner. In particular, our scheme adopts verifiable shuffling to establish a unified and trustworthy inter-domain pseudonym management approach that can preserve UE identity privacy and pseudonym unlinkability without relying on a trusted third party, and introduces a unified inter-domain group key management method based on Chinese remainder theorem and blockchain that significantly guarantees the flexibility, efficiency and trustworthiness. We formally prove the security of DeGKM and show its efficiency through simulations and comparisons with related works.

In the context of drone swarms, achieving efficient group secure communication is a challenging problem, due to the inherent limitations imposed by the drones’ limited energy and constrained resources. Physical layer group key generation (PLGK) is a promising technology to enable efficient group security communication. However, most existing PLGK schemes struggle to adapt to the dynamic nature of drone swarms. To address this gap, this paper proposes a novel satellite cluster state information (SCSI)-based PLGK, which leverages signal status information from all visible navigation satellites to establish the group key. The presented method utilizes the regional similarity of SCSI as a random information source to generate group keys between different drones, and employs a novel updating framework based on a fuzzy generator and a hash chain to enhance key update and alignment robustness. The proposed scheme not only significantly reduces the overhead of group key generation also mitigates the issues of key loss and reconstruction. The security of the proposed scheme is validated through formal protocol security proof and security analysis against possible attacks. Finally, experiments with real-world drones demonstrate the efficiency and effectiveness of the SCSI-based PLGK.

The rapid development of artificial intelligence (AI) has brought about revolutionary changes to industrial wireless networks. Meanwhile, these AI models have also incurred a more complex security environment. This article will investigate the new situations that may arise in the industrial radio security under the support of AI technologies. First, typical radio threats and the uniqueness of industrial wireless networks are introduced. We then review existing industrial wireless physical-layer security schemes based on various AI models from the perspective of countering these radio threats. From the attackers' perspective, three typical case studies are introduced, in which AI technologies will aid in jamming, spoofing, and eavesdropping attacks. Finally, we discussed the openness issues and potential solutions in industrial radio security. This article is of significant in understanding the current status of AI-based industrial radio security, as well as the main problems and challenges. This investigation can promote the healthy development of smart factories and future industries.

While smart grid significantly facilitates energy efficiency by using users’ power consumption data, it poses privacy leakage risk for user personal behaviors. Differential privacy (DP) has emerged as a promising solution to address this issue. However, existing approaches suffer from severe data utility degradation due to the intensive noise introduced by DP. Additionally, some of these methods are vulnerable to security attacks. To bridge this gap, in this paper, we propose ABDP (accuratebilling-enableddifferentiallyprivate), a mechanism that achieves high-strength DP while ensuring accurate aggregation and billing operations without compromising security. In particular, we propose aggregated and individual noise cancellation algorithms to counteract the negative effects of noise on data utility. Specifically, our ABDP ensures precise aggregation and accurate billing calculations for the power grid and individual users, respectively Furthermore, we present a Blockchain smart contract exploiting the pseudo random function to enforce a fair and secure data reporting process. Theoretical analysis is provided to evaluate the privacy and security guarantees of ABDP. Experimental results on real-world datasets, namely NERL-DATA and REDD, demonstrate that ABDP achieves error-free aggregation and billing calculation, offers arbitrary intensity privacy protection against non-intrusive load monitoring and filtering attacks, and outperforms existing state-of-the-art approaches.

Visual Question Answering (VQA) is the vision-language task of answering text-based questions presented in an image and has been advanced by the remarkable success of multimodal deep networks. Similar to unimodal networks, multimodal VQA models are also vulnerable to adversarial examples, which raises severe threats to the corresponding applications. Although several adversarial training methods have been proposed, most of them focus on improving the generalization ability of VQA models on clean samples instead of mitigating the adversarial attacks. In this paper, we systemically analyze the core structure of multimodal VQA networks and propose a novel adversarial training algorithm to mitigate adversarial attacks on VQA models. Specifically, our key component is a regularization term with our carefully designed Contrastive Fusion Representation (CFR), which can reduce the sensitivity of VQA models to adversarial perturbations of both the vision and language inputs. We further enhance the adversarial training with augmented CFRs. Comprehensive experimental results show that our method can mitigate adversarial attacks as well as preserve the generalization ability on clean samples under various system settings and outperforms other defense methods.

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

Physical-layer fingerprinting is a promising technique to identify Internet of Things (IoT) devices. In this article, we investigate a new radio-frequency (RF) fingerprinting based on the distinctive signal-to-noise-ratio (SNR) trace in the sector-level sweep (SLS) procedure of 5G IEEE 802.11ad devices. This SLS SNR trace-based fingerprinting can directly apply to off-the-shelf devices without any extra hardware requirements and be independent of the wireless channel and environment. To tackle the impact of orientation on the RF fingerprinting, we propose a novel fingerprinting framework, involving correlation analysis, surface fitting, curve pursuing, and binary classification, named the CSCB framework. Using this framework, the proposed SLS SNR trace-based fingerprinting can achieve device authentication at any orientation with one receiver under line-of-sight (LOS) or non-LOS (NLOS) scenarios. We conduct proof-of-concept experiments using off-the-shelf IEEE 802.11ad devices (Talon AD7200 and MG360 WiGig) to evaluate the performance of the proposed fingerprinting schemes. Experimental results show the effectiveness of the proposed fingerprinting schemes where the verification accuracy of the proposed scheme can reach 99% with only 200 training samples.

Wirelessly powered backscatter communications (WPBC) is an emerging technology for providing continuous energy and ultra-low power communications. Despite some progress in WPBC systems, resource allocation for multiple devices towards secure backscatter communications (BC) and efficient-energy harvesting (EH) requests a deep-insight investigation. In this paper, we consider a WPBC system in which a full-duplex access point (AP) transmits multi-sinewave signals to power backscatter devices (BDs) and injects artificial noise (AN) to secure their backscatter transmissions. To maximize the minimum harvested energy and ensure fairness and security of all BDs, we formulate an optimization problem by jointly considering the backscatter time, power splitting ratio between multi-sinewave and AN, and signal power allocation. For a single-BD system, we characterize the achievable secrecy rate-energy region with a non-linear energy harvester and propose two algorithms to solve an energy maximization problem. We then analyze the effect of multi-sinewave and AN signals on BD’s secrecy rate and harvested energy through simulations and proof-of-concept experiments. For a multi-BD system, we propose an iterative algorithm by leveraging block successive upper-bound minimization (BSUM) techniques to solve the non-convex problem of fair resource allocation and show its convergence and complexity. Numerical results show the proposed algorithm achieves optimal and equitable harvested energy for all BDs with satisfying the security constraint.

We study security of remote state estimation in wireless cyber-physical systems (CPS) where a sensor sends its measurements to the remote state estimator over a multichannel wireless link in presence of a jamming attacker. Most of the existing works study the sensor's defense scheme by adopting optimization-based methods and rely on the prior knowledge of the attacker's attack policy. To relax this constraint, we propose a novel online-learning-based policy called joint channel and power selection (J-CAP) for the sensor to dynamically choose transmission channel and power. The proposed method assumes no prior knowledge of the attacker's attack policy, nor of the channel state information. J-CAP jointly optimizes sensor's channel selection and power consumption, and guarantees the estimator's asymptotic stability. We theoretically prove that J-CAP achieves a sublinear learning regret bound. We also show J-CAP's optimality by deriving and matching its regret lower and upper bound orders. Compared with the solution that directly applies the baseline solution, J-CAP improves the regret upper bound by a factor of √{K+L}, where K and L denote the number of channels and number of power levels, respectively. Numerical evaluations validate the analytical results under various CPS parameters, and compare the J-CAP's performance with the state-of-the-art solutions.

Grant-free random access is an emerging technology for providing massive connectivity for 5G massive machine-type communications (mMTC), where non-orthogonal pilot sequences are used to simultaneously detect active users and estimate channels. However, grant-free 5G IoT networks are vulnerable to pilot contamination attacks (PCA), where the attacker can send the same pilots as legitimate IoT users to harm the active user detection and channel estimation. To defend against this attack, in this article, we propose a physical-layer countermeasure based on the channel virtual representation (CVR). CVR can emphasize the unique characteristics of mmWave channels that are sensitive to the location of the sender. This can be utilized to counter PCA no matter if the attacker's pilots are superimposed to that of the victim or not. Based on this observation, to achieve an efficient PCA detection, a single-hidden-layer multiple measurement (SHMM) Siamese network is employed. This solution tackles the challenges of channel randomness and massive connectivity in mMTC IoT networks, and supports small sample learning. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme under various scenarios. The detection accuracy can approach 99% with 128 antennas at the receiver and reach above 95% even with only 50 training samples.

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, to achieve efficient PL-SAD in 5G networks, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. To achieve spoofing attack detection, we provide two methods based on different machine learning models. For the first method, the detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). Another method involves a Siamese network, in which the similarity between sample pairs from one device is used to achieve PL-SAD. It can tackle the training problem that the historical data cannot support the identification of the same device in a new communication session. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD schemes. Experimental results confirm the effectiveness of the proposed PL-SAD schemes, and the detection accuracy can reach 99% using small sample sizes under different scenarios.

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process, to achieve efficient PL-SAD. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. The detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). It can tackle small sample learning and allow for quick model construction. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD scheme. Experimental results confirm the effectiveness of the proposed PL-SAD scheme, and the detection accuracy can reach 98% using small sample sizes under different scenarios.

Nonorthogonal multiple access (NOMA) technology can significantly promote Internet-of-Things (IoT) networks on spectral efficiency and massive connectivity. However, NOMA-IoT communications are vulnerable to pilot contamination attacks, where the attacker can send the same pilot signals as legitimate IoT users. Most existing countermeasures to this physical-layer threat struggle to adapt to NOMA-IoT networks, in which superimposed signals appear and low-cost IoT devices exist. In this article, we propose a compressed-sensing-based detection scheme to defend against pilot contamination attacks in NOMA-IoT networks. In particular, we present a multiple measurement vector (MMV) compressed sensing model and a security spreading code generation (SSCG) framework to prevent pilot contamination attacks from spoofing base station (BS) in NOMA-IoT networks. Furthermore, to efficiently reconstruct the superimposed signals based on the SSCG framework, a matching pursuit (MP) multiple response sparse Bayesian learning (MSBL) algorithm (MP-MSBL) is proposed. The security analysis and algorithm complexity of the proposed algorithms are provided. The simulation results evaluate and confirm the effectiveness of the proposed detection schemes. The reconstruction and detection accuracy of pilots can be higher than 99% under different scenarios.

The consumer unmanned aerial vehicle (UAV) market has grown significantly over the past few years. Despite its huge potential in spurring economic growth by supporting various applications, the increase of consumer UAVs poses potential risks to public security and personal privacy. To minimize the risks, efficiently detecting and identifying invading UAVs is in urgent need for both invasion detection and forensics purposes. Aiming to complement the existing physical detection mechanisms, we propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use Wi-Fi links for control and video streaming. The proposed framework extracts features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can efficiently detect UAVs and identify their operation modes. In order to reduce the online identification time, our framework adopts a re-weighted ℓ1-norm regularization, which considers the number of samples and computation cost of different features. This framework jointly optimizes feature selection and prediction performance in a unified objective function. To tackle the packet inter-arrival time uncertainty when optimizing the trade-off between the detection accuracy and delay, we utilize maximum likelihood estimation (MLE) method to estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of eight types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method. Evaluation results show that our proposed method can detect and identify tested UAVs within 0.15-0.35s with high accuracy of 85.7-95.2%. The UAV detection range is within the physical sensing range of 70m and 40m in the line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively. The operation mode of UAVs can be identified with high accuracy of 88.5-98.2%.

Power non-orthogonal multiple access (NOMA) has been considered as a new enabling technology in 5G communication. In this paper, we introduce the problem of pilot contamination attack (PCA) on NOMA in millimeter wave (mmWave) and massive MIMO 5G communication. Due to the new characteristics of NOMA such as superposed signals with multi-users, PCA detection faces new challenges. By harnessing the sparseness and statistics of mmWave and massive MIMO virtual channel, we propose two effective PCA detection schemes for NOMA tackling static and dynamic environments, respectively. For the static environment, the problem of PCA detection is formulated as a binary hypothesis test of the virtual channel sparsity. For the dynamic environment, the statistic of the peaks in the virtual channel is leveraged to distinguish the contamination state from the normal state. A peak estimation algorithm and a machine learning based detection framework are proposed to achieve high detection performance. To further optimize the proposed scheme, a feature selection algorithm and an optimization model considering the detection accuracy and detection delay are presented. Simulation results evaluate and confirm the effectiveness of the proposed detection schemes. The detection rate can approach 100% with 10-3false alarm rate in the static environment and above 95% in the dynamic environment under various system parameters.

In this paper, we consider a wireless powered backscatter communication (WPBC) network in which a full-duplex access point (AP) simultaneously transmits information and energy signals by injecting artificial noise (AN) to secure the backscatter transmission from multiple backscatter devices (BDs). To maximize the minimum throughput and ensure fairness and security, we formulate an optimization problem by jointly considering the power splitting ratio between dedicated information signals and AN, backscatter time and signal power allocation among multiple BDs. For a single BD network, we obtain a closed-form solution and evaluate its validity through proof-of-concept experiments. For the general case with multiple BDs, we present an iterative algorithm by leveraging block coordinate descent (BCD) and successive convex approximation optimization to solve a non-convex problem incurred in WPBC. We further show the convergence of the proposed algorithm and analyze its complexity. Finally, extensive simulation results show that the proposed algorithm achieves an optimal and equitable throughput for all BDs, and our work provides a good perspective of resource allocation to improve the performance of WPBC networks.

The fifth generation (5G) wireless technologies serve as a key propellent to meet the increasing demands of the future Internet of Things (IoT) networks. For wireless communication security in 5G IoT networks, physical-layer security (PLS) has recently received growing interest. This paper aims to provide a comprehensive survey of the PLS techniques in 5G IoT communication systems. The investigation consists of four hierarchical parts. In the first part, we review the characteristics of 5G IoT under typical application scenarios. We then introduce the security threats from the 5G IoT physical-layer and categorize them according to the different purposes of the attacker. In the third part, we examine the 5G communication technologies in 5G IoT systems and discuss their challenges and opportunities when coping with physical-layer threats, including massive multiple-input-multiple-output (MIMO), millimeter wave (mmWave) communications, nonorthogonal multiple access (NOMA), full-duplex technology, energy harvesting (EH), visible light communication (VLC), and unmanned aerial vehicle (UAV) communications. Finally, we discuss open research problems and future works about PLS in the IoT system with technologies of 5G and beyond.

In this work, we present a scheme of physical layer secret key generation for Millimeter wave (mmWave) Massive MIMO system. Our scheme is compatible with current hardware structure and protocols including Analog Beamforming, Massive MIMO, and Beam Sweep. We add a small perturbation angle into the Angle of Arrival (AoA) of the transmitter as the common randomness, which significantly improved the secret key rate without being constrained by the complexity of link initialization protocols and low dynamic of the channel. Therefore, the secret key rate can be enhanced by increasing the number of perturbations. In addition, our scheme can combat co-located eavesdropper (Eve) by utilizing the high directionality of Massive MIMO antenna. Numerical results show that our scheme has a high bit agreement ratio (BAR) between legitimate users while the co-located Eve only gets the BAR around 50%, which indicates that the secrecy of the generated key is well achieved.

Physical layer security of non-orthogonal multiple access (NOMA) systems has attracted great attentions. However, the impact of mobility on physical layer security of NOMA systems has not been well studied. In this paper, to fill this gap, we investigate the impact of random mobility on physical layer security of NOMA systems. Considering scenarios where a base station (BS) or access point (AP) communicates to two random mobile users with a passive eavesdropper in two concentric circles, we study the secrecy performance with combinations of two typical random mobility models: random waypoint (RWP) and random direction (RD). A general analytical framework to numerically calculate the average secrecy rates of NOMA mobile users under steady state is provided. By comparing secrecy performance of mobile users with static users, we find that RWP mobile users can achieve higher average secrecy rates than the users with other mobility combinations. Meanwhile, two types of secrecy fairness for mobile users are fully considered and we propose a novel sum average secrecy rate maximization problem, subject to average power limits and users' QoS (quality of service) requirements. Considering eavesdropper's channel state information (CSI) is unknown to BS, we propose a threshold power allocation strategy to improve the sum average secrecy rate of NOMA mobile users. Extensive numerical simulations are conducted to validate our model and theoretical analysis.

In many IoT (Internet-of-Things) applications, a large number of low-cost IoT devices are connected to the Internet through an access point (AP) or gateway via wireless communication. Due to the resource constraints on IoT devices and broadcast nature of wireless medium, identity spoofing attacks are easy to launch but hard to defend in an IoT wireless access network. In this paper, under the context of 5G communication, we propose an efficient physical layer identity spoofing attack detection scheme for IoT. By harnessing the sparsity of the virtual channel in mmWave and Massive MIMO 5G communication, we propose a two- step detection scheme. In the first step, our scheme detects anomalies by examining the virtual angles of arrival (AoA) and path gains of all the IoT devices simultaneously in a virtual channel space (VCS). In the second step, we introduce a machine learning based detection scheme to detect the actual attack. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme. The minimum Bayes risk of the proposed scheme can be less than 0.5\% even in the presence of 100 IoT devices.

In this study, the authors consider secure communications in multiuser wireless networks where full‐duplex (FD) jamming operates to enhance physical layer security. The considered multiuser system is equipped with FD legitimate receivers in contrast to conventional frameworks where a half‐duplex (HD) receiver is at hand. This study investigates an alternative solution in which the authors take advantage of FD capability of the receivers to send jamming signals against the eavesdropper. Under these assumptions, the impact of self‐interference and channel interference on physical layer security is investigated. They derive the expressions of secrecy outage probability and ergodic secrecy rate in the proposed system. Two FD jammer selection schemes are proposed to further improve the security. In addition, they apply a reinforcement learning technology, called Q‐learning, to model the interaction between the source and multiple jamming users. The preliminary results show that the application of FD jamming and user selection scheme leads to a significant improvement in the wireless network security.

Information security is a vital concern in Internet of Things (IoT). Traditional security method based on public or private key encryption scheme is limited by the trade‐off between low cost and high level of security. Among different security solutions, utilising compressed sensing (CS) in combination with the physical‐layer security to achieve the security is a remarkable method. However, in the current literatures, little attention has been given to the area of static environment, which will lead the risk of information leakage in the CS security model. In this study, the authors propose a new CS security model, in which circulant matrix is exploited to improve the generation efficiency of the measurement matrix, and binary resilient functions are utilised to enhance the security. Furthermore, considering the practical application, they present a feasible framework, named CS security scheme based on frequency‐selective, where the frequency‐selective feature of the wireless channel is applied to support the static environment. To verify the effectiveness of the proposed scheme, they conducted experiments and numerical simulations to evaluate the performance, and the results are satisfactory.

Spoofing attacks is one of the most critical attacks in wireless communication security. Traditional solutions are based on cryptology which is performed in the upper layers, and face many challenges especially in resource‐limited application. To overcome this hurdle, physical‐layer security has been received a lot of attention recently. In this study, the authors propose a physical‐layer spoofing detecting scheme, where signal processing and feature recognition are utilised to improve the detection performance. In this study, they present a pretreatment process based on sparse representation (SR) to reinforce the characteristic of the signal. Furthermore, they formulate the problem of spoofing detection as one of the feature extraction and recognition, and employ a developed fuzzy C‐mean algorithm to further increase the recognition accuracy. In addition, in order to verify the proposed method, they conduct experiments and use numerical simulation and analysis to evaluate the detection performance. Results showed that the proposed approach can improve the recognition accuracy significantly (increased by one order of magnitude) and the complexity is acceptable (polynomial complexity). Their findings showed that combining SR and feature extraction and recognition, the proposed method provided a good access to achieve a higher accuracy scheme of spoofing detection.

Sparse signal recovery or compressed sensing (CS) theory has recently received attention in the image compression field. CS has proven that the successful recovery rate of the multiple measurement vectors (MMVs) model is higher than that of the single measurement vector (SMV) case. Most existing algorithms have focused on sparse signal recovery using the MMV model, without considering converting the general SMV model into the MMV model. In this study, a simple transforming model that takes advantage of the correlations among wavelet coefficients is proposed, such that the MMV model can be used for general images rather than only certain special signals. To further enhance the performance of the MMV model, the improved MMV model based on the similarity of image blocks is proposed. Simulation results have shown that the obtained solution matrix can be used in the MMV model, and that the proposed algorithm provides better reconstruction quality than many state‐of‐the‐art algorithms.

In this paper, we propose a new security design, called as Zero Reconciliation Secret Key Extraction, for backscatter wireless systems, in which a reader needs to establish secret keys for multiple tags. Our design is able to eliminate the reconciliation process in conventional physical layer based key establishment, therefore improving the efficiency while still maintaining security in such a process. The essence in our design is to use a channel state information (CSI) characteristic, named CSI Ratio, at the reader to differentiate multiple tags, then employ multiple-input multiple-output (MIMO) precoding for legitimate tags to effectively and securely establish secret keys, at the same time leveraging artificial jamming to forestall eavesdropping attacks in the network. We evaluate our design with real-world experimental data and show that the proposed approach can achieve relatively high secret key extraction rates and maintain low bit error rates.

Compressed Sensing (CS) theory has recently received amount of attention in the image compression filed. The sparser the signal has, the better the performance recovery. Most wavelet-based reconstruction methods of CS are developed under the assumption that the small wavelet coefficients are close to zero. In other words, a part of image information has been lost before measurement sampling. As we know, most of images have many similar areas. In order to avoid the image information being lost as little as possible, in this paper, a new CS scheme based on the similarity of image blocks is proposed in wavelet domain. Instead of processing the image as a whole, the image is firstly divided into small image blocks. And a clustering algorithm is presented to gather the similar image blocks into a group. Experiments on images demonstrate favorable performances of the proposed method.

In many wireless communication systems, data can be divided into different importance levels. For these systems, unequal error protection (UEP) techniques are used to ensure lower bit error rate for the more important classes. Moreover, if the precise characteristics of the channel are known, UEP can be used to correctly recover the more important classes even under severe receiving conditions. In this study, a UEP scheme based on compressed sensing via a linear program is proposed. Discrete wavelet transform (DWT) is chosen as the sparsifying basis, and then DWT‐coded information is divided into two‐layered coded streams, each of which is transmitted differentially by applying an unequal number of information bits in linear codes according to the time‐varying characteristic of the corrupted channel. In this proposed transmission scheme, the more important information is to guarantee error‐free transmission. At the decoder, one can simply reconstruct the signal via the l 1 ‐minimisation algorithm. Simulation results show that the proposed scheme can achieve a higher peak signal‐to‐noise ratio (PSNR) and obviously improve the error resilience compared to the equal error protection scheme and other UEP methods. More importantly, with the increase of channel corrupted ratio, the drop rate of PSNR is much slower than other solutions. It indicates that the proposed method has better robustness for severe channel conditions.