This paper addresses the spoofing detection issue for the Global Positioning System (GPS) based on radio frequency fingerprinting (RFF). We first introduce a new RFF feature in the post-despreading domain of GPS signal processing to better capture the hardware characteristics of GPS satellites. We model the new RFF feature as grayscale constellation images (GCIs) and provide in-depth analysis to show the hardware characteristics that can be captured by GCIs. Using this feature, we develop a deep-learning based spoofing detection framework named GCI-GANomaly, which applies a generative adversarial network (GAN) with a cosine latent anomaly scoring strategy for robust detection. We evaluate the detection accuracy and false positive rate (FPR) of the proposed method based on the open-source Texas Spoofing Test Battery (TEXBAT) dataset. The results showed that GCI-GANomaly improves the detection accuracy of traditional signal quality monitoring (SQM)-based methods by up to 30.8% and reduces the average FPR of existing RFF-based methods by 4.2% with much less training data while achieving slightly better average detection accuracy. We further evaluate the robustness (against spoofing power and time) of GCI-GANomaly based on a specific GPS signal dataset that we collected from the real-world constellation. The results showed that GCI-GANomaly achieves robust detection performance under varying spoofing power and shows acceptable stability as time elapses.

Traditional physical layer authentication (PLA) schemes in wireless networks typically depend on individual nodes for feature observation, lacking cooperative gain and thus suffering from limited accuracy and robustness. This paper investigates a cooperative transmission and PLA framework for wireless networks, wherein multiple legitimate devices serve as both message relays and identity verifiers by extracting hardware fingerprints. We first establish a theoretical model for the system’s bit error rate (BER), false alarm rate (FAR), and detection probability (PD), and derive closed-form upper bounds to characterize the transmission reliability and authentication performance. Based on our theoretical model, we then define an accuracy improvement ratio (AIR) metric that quantifies FAR gain without compromising BER and PD performance, and derive channel conditions to ensure a positive AIR. To validate the proposed integrated framework, a time-division multiple access (TDMA)-based case study is conducted using carrier frequency offset as the authentication feature. Simulation results demonstrate that the proposed scheme can reduce FAR by up to 100.0% under favorable signal-to-noise ratio (SNR) conditions, while maintaining the BER and PD performance of the conventional non-cooperative scheme, thereby confirming its effectiveness for enhancing secure wireless communications.

The fifth-generation mobile communication technology (5G) supports wireless data transmission across civil, commercial, industrial, and even military networks, where vast amounts of privacy data are constantly transmitted. However, the open nature of the air interface in 5G systems makes them vulnerable to various attacks. Physical-layer key generation (PKG) has been recognized as a highly promising technology for ensuring data security in 5G systems, while existing PKG schemes achieve low key entropy (i.e., low key randomness) due to poor channel probing and quantization. In this paper, we propose a PKG scheme with high key entropy, tailored to the unique characteristics of 5G systems. First, we design a channel probing method based on the demodulation reference signal in accordance with 5G standards, enhancing the similarity between channel measurements. Next, we introduce a quantization method based on local increment and monotonicity, which effectively leverages channel characteristics to achieve high-speed key generation and improve key entropy. Finally, we use both MATLAB simulation and real-world channel measurements to achieve comprehensive verification of the proposed scheme. The simulation results showed that the proposed scheme increases the key entropy by at least 25% with nearly the same key generation rate compared with existing PKG schemes for 5G systems. The experiment using real-world channel measurements also confirmed that the proposed scheme has higher key entropy.

This paper investigates secure communication in multi-UAV networks, where each UAV employs rate-splitting multiple access (RSMA) to simultaneously deliver downlink data services to multiple ground terminals (GTs) under eavesdropping threats. To enhance network security, we propose a two-stage collaborative RSMA transmission scheme. Based on this scheme, we study the optimization of multi-UAV cooperative trajectory, time-step sharing and jamming power (MUCTSJ) to maximize the network’s secrecy rate. Additionally, to ensure fairness in throughput allocation among GTs, we incorporate two typical UAV service principles—Channel Quality First (CQF) and Fair Service First (FSF)—into the optimization objectives. Given the non-convex and NP-hard nature of this optimization problem, we reformulate it as a Markov Decision Process (MDP) and introduce a multi-agent reinforcement learning (MARL) framework based on the Centralized Training and Decentralized Execution (CTDE) paradigm. To address the dynamic topological changes induced by UAV mobility and time-varying channel states, as well as the gradient interference among multiple learning tasks, we design a Multi-Task Attention Deep Recurrent Network (MTA-DRNN). This architecture effectively captures the distinct observed attributes of each UAV while enhancing the coordination between diverse actions, thereby improving the adaptability of the agent and the stability of training. Simulation results demonstrate the superiority of the proposed solution enhances the security of multi-UAV networks over other baseline schemes. Furthermore, deployment on corresponding hardware platforms confirms the solution’s effectiveness and robustness in practical applications.

Authentication in millimeter-Wave (mmWave) Multiple-Input Multiple-Output (MIMO) systems is a critical issue due to the unique characteristics of mmWave communication, such as highly directional beamforming and the ability to support massive device connectivity. To address this challenge, this paper proposes a novel low-complexity decision-level-based Distributed Physical Layer Authentication (DPLA) framework to combat identity-based impersonation attacks in mmWave MIMO systems. The DPLA framework leverages Beam Pattern (BP) deviation, which arises from hardware-specific gain errors, as a key authentication feature. A fusion center is introduced to make the final authentication decision by aggregating local decisions from multiple collaborative nodes, enabling multi-directional perception. Specifically, a low-complexity hybrid combining fusion rule is carefully designed to accommodate the fully connected structure of mmWave MIMO systems, balancing computational efficiency and authentication performance. A rigorous performance analysis is conducted by deriving closed-form analytical expressions for the probabilities of correct detection and false alarm. Furthermore, the asymptotic detection and discrimination performance are systematically analyzed in the large-scale antenna regime. To further enhance authentication accuracy, digital signaling matrices are designed using the deflection coefficient maximization principle. The feasibility of the proposed framework is validated through a comprehensive evaluation, demonstrating its superior robustness and efficiency compared to benchmark methods.

This paper examines the enhancement of secrecy energy efficiency (SEE) in a reconfigurable intelligent surface (RIS)-assisted two-way relay (TWR) system. We first establish a theoretical model for the system's secrecy rate, energy consumption, and SEE, and formulate the SEE maximization problem through the joint design of the RIS phase shifts and beamforming matrix. Using techniques such as weighted minimum mean square error (WMMSE), alternating optimization, and the augmented Lagrange method, we then develop a theoretical framework that identifies locally optimal solutions for the RIS and beamforming settings under unit-modulus and power constraints. The proposed framework is also shown to be applicable to solving the system's secrecy rate maximization problem. To address the computational complexity involved in optimizing the RIS phase shifts, we further propose a suboptimal scheme leveraging the Newton's method, which significantly reduces the computational burden while achieving performance close to the optimal SEE. Extensive numerical results validate the effectiveness of the proposed schemes, showing significant SEE improvements compared to traditional channel-capacity-based secure transmission scheme.

This paper explores a new secure wireless communication scenario for the data collection in the Internet of Things (IoT) where the physical layer security technology is applied to counteract both the detection and eavesdropping attacks, such that the critical covertness and secrecy properties of the communication are jointly guaranteed. We first provide theoretical modeling for covertness outage probability (COP), secrecy outage probability (SOP) and transmission probability (TP) to depict the covertness, secrecy and transmission performances of the wireless communication system. To understand the fundamental security performance under the wireless communication system, we then define a new metric -covert secrecy rate (CSR), which characterizes the maximum transmission rate subject to the constraints of COP, SOP and TP. We further conduct detailed theoretical analysis to identify the CSR under various scenarios determined by the detector-eavesdropper relationships and the secure transmission schemes adopted by transmitters. Finally, numerical results are provided to illustrate the achievable performances under the secure wireless communication system.

Satellites serve as a key component for the upcoming space-air-ground integrated networks, while their signals are susceptible to spoofing attacks. Radio frequency fingerprinting (RFF) has been recognized as a highly promising authentication approach to counteracting spoofing attacks. Despite extensive RFF schemes proposed for terrestrial networks, RFF for satellites remains largely unexplored except for a recently reported scheme named PAST-AI, which exploits the spatial property of the IQ imbalance of downlink signals to authenticate Iridium satellites. Although PAST-AI demonstrates the potential of RFF for satellite authentication, its authentication accuracy and time are unsatisfactory. To address this issue, this paper proposes a novel 3D convolution-based RFF scheme for Iridium satellite authentication, which exploits not only the spatial property but also the temporal property of the IQ imbalance. The proposed RFF scheme transforms short-period sequences of successive IQ samples into 3D data samples and uses a 3D convolutional neural network (CNN) to train an RFF model. To evaluate the authentication accuracy, we collected over 198000000 IQ samples from all 66 Iridium satellites and generated 1000 3D data samples for each satellite. The results showed that the proposed RFF scheme achieves more accurate authentication than PAST-AI using fewer IQ samples (i.e., shorter time).

Satellite internet serves as an indispensable component of the upcoming sixth‐generation networks for providing global broadband internet access service. Due to the open nature of satellite‐ground communication, security issue in satellite internet has always been an important concern for both industry and academia. Although many researchers focus on secure communications in satellite internet, the literature is surprisingly sparse, with no comprehensive overview of the state‐of‐the‐art security techniques. This paper provides an in‐depth survey of secure communications for various satellite internet scenarios. Based on different security mechanisms, we first categorize the existing works of secure communications in satellite internet into two categories: cryptography‐based and physical layer security‐based. The former includes classical encryption‐based and quantum encryption‐based secure communication, and the latter is further divided into precoding‐based, cooperative jamming‐based, relay selection‐based, and physical‐layer authentication‐based secure communication depending on the applied techniques. Finally, we provide some future research directions.

Auction is becoming increasingly popular for dynamic spectrum access (DSA), while it is extremely vulnerable to sybil attacks. Existing studies on sybil-proof DSA auction impractically assume that attackers bid truthfully based on true appraisals. This paper, for the first time, considers untruthful attackers and investigates the sybil-proof auction design in such more hazardous scenarios. To justify the new assumption, we first show that attackers obtain higher utilities by bidding untruthfully, especially in networks with inadequate channels. Based on this novel finding, we then design a practical sybil attack model named EqualSumBid Sybil, where attackers follow an equal-sum rule (i.e., the sum bid value of the multiple identities of an attacker equals the bid value when it bids with only one identity) instead of their true appraisals. To ensure efficient DSA under the new attack, we finally propose the PRAM, a Practical sybil-pRoof Auction Mechanism, where suspicious identity merging and bid-independent bidder sorting methods are introduced to alleviate the effect of untruthfulness on spectrum auction. Furthermore, winner selection and payment methods are designed to resist the EqualSumBid Sybil attack. Theoretical analyses and numerical results show that PRAM not only resists the EqualSumBid Sybil attack but also achieves individual rationality and truthfulness.

While the millimeter-wave (mmWave) communication is less susceptible 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 attacker 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.

Millimeter-wave (mmWave) communications are highly promising to improve the capacity of modern wireless networks, while the physical layer security (PLS) techniques hold great potential to enhance the critical secrecy performance therein. By carefully exploiting the significant signal difference between the Non-Light-of-Sight (NLoS) and Line-of-Sight (LoS) mmWave links, this paper proposes a Sight-based Cooperative Jamming (SCJ) scheme to improve the PLS performance of mmWave ad hoc communications. In this scheme, each potential jammer that has no LoS link to its nearest receiver but may have LoS links to eavesdroppers is selected with a certain probability to generate artificial noise such that channel advantages at legitimate receivers can be achieved. For performance modeling of the new jamming scheme, novel and efficient theoretical approximation approaches are firstly developed to enable the challenging issue of interference distribution modeling to be tackled, and then a theoretical framework based on stochastic geometry is proposed to capture the secrecy transmission capacity behavior under the SCJ scheme. Finally, extensive numerical results are provided to illustrate the SCJ scheme under various network scenarios.

Blockchain-based access control (BBAC) has been highly promising to prevent unauthorized resource access in the Internet of Things (IoT). However, maintaining BBAC can be potentially expensive due to the storage cost of the blockchain. To address this issue, we propose a layered BBAC architecture by combining blockchain with blockchain oracle and tamper-proof decentralized storage (e.g., IOTA). The proposed architecture consists of three main layers: a blockchain layer, which provides distributed and trustworthy access control, a storage layer, which stores meta data (e.g., subject/object attributes and policies) used in the access control of the blockchain layer, and an oracle layer, which works as a bridge to help transfer data between the blockchain and decentralized storage. This architecture achieves robust, auditable, and cost-efficient access control by migrating the meta data from the blockchain to the decentralized storage while keeping the fascinating tamper-proof feature of the blockchain. We implement and evaluate this architecture in terms of time and monetary cost to demonstrate its feasibility and superiority over existing ones.

Efficient and reliable access control in smart cities is critical for the protection of various resources for decision making and task execution. Existing centralized access control schemes suffer from the limitations of single point of failure, low reliability, and poor scalability. This article, therefore, proposes a distributed and reliable access control framework for smart cities by combining the blockchain smart contract technology and the attribute-based access control (ABAC) model. The framework consists of one policy management contract (PMC) for managing the ABAC policies, one subject attribute management contract (SAMC) for managing the attributes of subjects (i.e., entities accessing resources), one object attribute management contract (OAMC) for managing the attributes of objects (i.e., resources being accessed), and one access control contract (ACC) for performing the access control. To show the feasibility of the proposed framework, we construct a local private Ethereum blockchain system to implement the four smart contracts and also conduct experiments to evaluate the monetary cost as well as to compare the proposed framework with an existing access control list (ACL)-based scheme. The experimental results show that although the proposed scheme consumes more money than the ACL-based scheme at the deployment stage, it introduces less monetary cost during the system running especially for large-scale smart cities.

Mobile ad hoc networks (MANETs) represent a class of important network models for supporting various critical applications, while the security breach due to eavesdropping attacks has been a critical issue. This study investigates the security issue of MANETs from the perspective of physical layer security (PLS). In particular, by combining PLS techniques [e.g. artificial noise (AN) injection and Secrecy Guard zoNe (SGN)] and the conventional Aloha protocol, the authors first propose an AN‐based Aloha protocol and a SGN‐based Aloha protocol to ensure secure medium access for legitimate transmitters. In the AN‐based Aloha protocol, all potential transmitters are allowed to be active and each active transmitter injects AN into its transmitted signals to confuse eavesdroppers. In the SGN‐based protocol, each potential transmitter has an SGN, a circle centred at itself, and only the potential transmitters whose SGN contains no eavesdroppers are allowed to be active. To understand the security performances of the proposed security‐aware Aloha protocols, the authors then apply tools from Stochastic Geometry to analyse the secrecy transmission capacity (STC) performances of MANETs under both protocols. Finally, the authors provide simulation/numerical results to corroborate the proposed theoretical analysis and also to show the impacts of network parameters on the STC performances.

The large-scale and trustless nature of the Internet of Things (IoT) calls for distributed and trustworthy access control schemes to prevent unauthorized resource access. This paper proposes a Capability-Based Access Control (CapBAC) scheme by applying the emerging Ethereum blockchain technology. This scheme uses Ethereum smart contracts, i.e., executable codes residing in the blockchain, to store and manage the capability tokens, i.e., special data structures that maintain the allowed actions of a user (i.e., subject) on a certain resource (i.e., object). To provide more fine-grained access control and more flexible token management, this scheme defines capability tokens in units of actions, i.e., by dividing a conventional capability token containing multiple actions into multiple ones with each being associated with a certain action. In addition, this scheme uses a delegation graph instead of the delegation tree in existing smart contract-based CapBAC schemes to store the token delegation relationship among the subjects. By storing the tokens and the delegation graph in smart contracts, this scheme allows object owners to verify the ownership and validity of the capability tokens of the subjects. To demonstrate the feasibility of the scheme, we constructed a local Ethereum blockchain network and conducted extensive experiments.

Access control has been recognized as a critical issue for preventing unauthorized access to the resources in Internet of Things (IoT) systems. This paper proposes an Attribute-Based Access Control (ABAC) framework for IoT systems by using the emerging Ethereum smart contract technology. The framework consists of one Policy Management Contract (PMC), one Subject Attribute Management Contract (SAMC), one Object Attribute Management Contract (OAMC) and one Access Control Contract (ACC). The PMC, SAMC and OAMC are responsible for storing and managing the ABAC policies, the attributes of subjects (i.e., entities accessing resources) and the attributes of objects (i.e., resources being accessed), respectively. When receiving access requests, the ACC retrieves the subject attributes and object attributes as well as the corresponding policy from the SAMC, OAMC and PMC to perform the access control. Combining the ABAC model and the blockchain technology, this framework is expected to achieve distributed, trustworthy and fine-grained access control for IoT systems. To show the feasibility of the proposed framework, we construct a local private Ethereum blockchain system to implement the four smart contracts and also conduct experiments to test the monetary and time cost.

This paper investigates a critical access control issue in the Internet of Things (IoT). In particular, we propose a smart contract-based framework, which consists of multiple access control contracts (ACCs), one judge contract (JC), and one register contract (RC), to achieve distributed and trustworthy access control for IoT systems. Each ACC provides one access control method for a subject-object pair, and implements both static access right validation based on predefined policies and dynamic access right validation by checking the behavior of the subject. The JC implements a misbehavior-judging method to facilitate the dynamic validation of the ACCs by receiving misbehavior reports from the ACCs, judging the misbehavior and returning the corresponding penalty. The RC registers the information of the access control and misbehavior-judging methods as well as their smart contracts, and also provides functions (e.g., register, update, and delete) to manage these methods. To demonstrate the application of the framework, we provide a case study in an IoT system with one desktop computer, one laptop and two Raspberry Pi single-board computers, where the ACCs, JC, and RC are implemented based on the Ethereum smart contract platform to achieve the access control.

This paper investigates the fundamental issues of mode selection and spectrum partition in cellular networks with in-band device-to-device (D2D) communication from the physical-layer security (PLS) perspective. We consider a mode selection scheme allowing each D2D pair to probabilistically switch between the underlay and overlay modes, and also a spectrum partition scheme where the system spectrum is orthogonally partitioned between cellular and overlay D2D communications. We first develop a general theoretical framework to model the secrecy outage and secrecy capacity performance of cellular users as well as the outage and capacity performance of D2D pairs. Optimization problems are also solved to identify the optimal mode selection and spectrum partition for secrecy capacity maximization and secrecy outage probability minimization. A case study is then provided to demonstrate the application of our theoretical framework for performance modeling and optimization, and also to illustrate the impacts of mode selection and spectrum partition on the PLS performances of in-band D2D communications.

Service Oriented Computing (SOC) has initially developed for the Internet, but also identified as an appealing computing paradigm for developing applications in distributed wireless environments. The open nature of wireless medium may expose services to a variety of unauthorized third parties (eavesdroppers), resulting in insecure service interactions, while cooperative jamming is promising to provide a strong form of security. This paper focuses on security performance study of wireless communications for service interactions among different parties in SOC. More specifically, this paper establishes a theoretical framework for the study of eavesdropper-tolerance capability (i.e., the maximum number of eavesdroppers that can be tolerated) in a two-hop wireless network, where the cooperative jamming is adopted to ensure security defined by secrecy outage probability (SOP) and opportunistic relaying is adopted to guarantee reliability defined by transmission outage probability (TOP). For the concerned network, exact modeling for SOP and fine approximation for TOP are first conducted based on the Central Limit Theorem. With the help of SOP and TOP models and also the Stochastic Ordering Theory, the model for eavesdropper-tolerance capability analysis is then developed. Finally, extensive simulation and numerical results are provided to illustrate the efficiency of our theoretical framework as well as the eavesdropper-tolerance capability of the concerned network from adopting cooperative jamming and opportunistic relaying.

This paper investigates the security-delay trade-off of the buffer-aided relay selection scheme in a two-hop wireless system, which consists of a source-destination pair, one eavesdropper, and multiple relays each having a finite buffer. To evaluate the security and delay performances of the system, we derive analytical expressions for the end-to-end (E2E) secure transmission probability (STP) and the expected E2E delay under both perfect and partial eavesdropper channel state information (CSI) cases. These analytical expressions help us to explore the inherent trade-off between the security and delay performances of the concerned system. In particular, the results in this paper indicate that: 1) the maximum E2E STP increases as the constraint on the expected E2E delay becomes less strict, and such trend is more sensitive to the variation of the number of relays than that of the relay buffer size; 2) on the other hand, the minimum expected E2E delay tends to decrease when a less strict constraint on E2E STP is imposed, and this trend is more sensitive to the variation of the relay buffer size than that of the number of relays.

Wireless communication is one of the key technologies that actualize the Internet of Things (IoT) concept into the real world. Understanding the security performance of wireless communications lays the foundation for the security management of IoT. Eavesdropper collusion represents a significant threat to wireless communication security, while physical-layer security serves as a promising approach to providing a strong form of security guarantee. This paper studies the important secrecy outage performance of wireless communications under eavesdropper collusion, where the physical layer security is adopted to counteract such attack. Based on the classical Probability Theory, we first conduct analysis on the secrecy outage of the simple noncolluding case in which eavesdroppers do not collude and operate independently. For the secrecy outage analysis of the more hazardous M-colluding scenario, where any M eavesdroppers can combine their observations to decode the message, the techniques of Laplace transform, keyhole contour integral, and Cauchy Integral Theorem are jointly adopted to work around the highly cumbersome multifold convolution problem involved in such analysis, such that the related signal-to-interference ratio modeling for all colluding eavesdroppers can be conducted and thus the corresponding secrecy outage probability can be analytically determined. Finally, simulation and numerical results are provided to illustrate our theoretical achievements. An interesting observation suggests that the SOP increases first superlinearly and then sublinearly with M.