This paper proposes a novel Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive Internet of Vehicles (CIoV) network under malicious jamming attacks, where the IRS enhances communication performance by establishing additional links. In order to maximize the sum transmission rate of Vehicle-to-Infrastructure (V2I) links, we propose an optimization problem that jointly optimizes wireless resource allocation, such as spectrum and transmit power for Vehicle Users (VUs) and IRS phase shift. Because this problem is non-convex and complicated, we further propose a Heterogeneous Multi-agent Transformer-enhanced Dueling Double Deep Q-Network (HMA-TD3QN) based resource allocation method, where VUs and Secondary Base Station (SBS) act as distinct heterogeneous agents can independently perform resource allocation and phase shift optimization. The Transformer neural network architecture can better adapt to long sequence input states and extract relevant features from complex input states through the attention mechanism. Simulation results indicate that the proposed HMA-TD3QN method achieves improvements of 24.42%, 20.79%, and 22.25% over the basic HMA-DQN under three different jamming strategies, highlighting the effectiveness of IRS technology in enhancing the Quality of Service (QoS) and jamming resilience of CIoV network.

Industrial Power Internet of Things (IPIoT) integrates millions of smart devices for real-time grid monitoring, yet faces growing intrusion threats that compromise operational safety. Existing detection systems struggle with resource constraints, real-time requirements, and harsh electromagnetic environments typical of industrial substations. This article proposes graph convolutional-attention network with fuzzy cerebellar model articulation controller (GCAT-FCMAC) model, a lightweight graph neural network combining graph convolutional networks (GCN), graph attention networks (GAT), and fuzzy cerebellar model articulation controller (FCMAC) for efficient intrusion localization on edge devices. GCN and GAT extract multiscale network topology features, while FCMAC adaptively fuses heterogeneous embeddings via fuzzy inference. Experiments on simulated and real-world IPIoT datasets show that GCAT-FCMAC reduces average localization error by 47% over the best-performing baseline GCAT and energy consumption by 53%, while requiring only 138 k parameters, with the FCMAC fusion layer achieving an eightfold compression of the joint embedding space. On simulated 300-node networks and Raspberry Pi 4 edge deployment, the method achieves 92.1% and 92.8% accuracy at 22 and 34.2 ms inference latency, respectively, confirming its applicability to smart grids and manufacturing systems.

Cognitive Internet of Vehicles (CIoV) adds the cognitive engine based on traditional Internet of Vehicles (IoV), which can improve spectrum utilization. However, spectrum sensing data falsification (SSDF) attacks pose a threat to CIoV network security. To ensure the full utilization of spectrum resources and protect primary users transmission, this article combines blockchain with CIoV to defend against SSDF attacks in the presence of vehicle users (VUs) entering and leaving the network. Specifically, this article introduces a virtual currency called Sencoins serve as credential for VUs to purchase transmission shares. And this article proposes a reward and punishment mechanism and a hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) mining model to thwart the motivation of the VUs to launch SSDF attacks. On this basis, this article investigates the dynamics of SSDF attack strategy choice of VUs, and uses the largest Lyapunov exponent (LLE) to determine the critical value of Sencoins that avoids the system to exhibit chaotic behavior. To describe the uncertainty of the population proportion of VUs that choose different attack strategies due to high-speed movement and the VUs entering and leaving the CIoV network, this article introduces Gaussian white noise into the replication dynamics equation and builds the It & ocirc; stochastic evolutionary game model, and solves it according to the stability judgment theorem of stochastic differential equations and stochastic Taylor expansion. Finally, simulation results verify that the proposed method can quickly and effectively thwart SSDF attacks in the CIoV network. And compared with traditional methods, the proposed method can improve the efficiency of defending against SSDF attacks by 567% and the average throughput by 25%.

This paper investigates a covert communication with symbiotic backscatter, based on a segmented simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), ensuring reliable communication between transmitter Alice and warden user Willie, while simultaneously enabling covert communication with user Bob. The structure of the STAR-RIS is segmented into a primary zone (P zone) and a covert zone (C zone), adopting the energy splitting (ES) and time switching (TS) protocols, respectively. Reliable transmission between Alice and Willie is maintained via the P zone. In contrast, the C zone conveys covert information to Bob by embedding the backscatter signal into the primary system signal during the transmission phase while further maintaining reliable communication with Willie during the reflection phase. By analyzing the connection outage probability (COP), the allocation of time resources between the two phases of the C zone is studied. Furthermore, under the constraint of covertness, both the allocation of elements and transmission/reflection coefficients are adjusted to optimize and achieve the highest effective covert rate. The results of the simulation reveal a notable improvement in covert communication performance due to the proposed system.

The Internet of Vehicles (IoV) relies on Vehicle-to-Everything (V2X) communications to enable cooperative perception among vehicles, infrastructures, and devices, where Vehicle-to-Infrastructure (V2I) links are crucial for reliable transmission. However, the openness of wireless channels exposes IoV to eavesdropping, threatening privacy and security. This paper investigates an Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive IoV (CIoV) network to enhance physical layer security in V2I communications. A non-convex joint optimization problem involving spectrum allocation, transmit power for Vehicle Users (VUs), and IRS phase shifts is formulated. To address this challenge, a heterogeneous multi-agent (HMA) Mamba RainbowDQN algorithm is proposed, where homogeneous VUs and a heterogeneous secondary base station (SBS) act as distinct agents to simplify decision-making. Simulation results show that the proposed method significantly outperform benchmark schemes, achieving a 13.29% improvement in secrecy rate and a 54.2% reduction in secrecy outage probability (SOP). These results confirm the effectiveness of integrating IRS and deep reinforcement learning (DRL) for secure and efficient V2I communications in CIoV networks.

This work proposes a six-dimensional movable antenna (6DMA) enhanced covert communication scheme, enabling flexible adjustments of both positions and rotations of 6DMA surfaces to improve covert performance. We first formulate an optimization problem to maximize effective throughput by jointly designing the positions, rotations, and transmit beamforming of the 6DMAs, subject to a covert constraint, a total power constraint, and the antenna spatial feasibility constraints. To address the non-convex optimization challenge, we employ an alternating optimization framework combined with semidefinite relaxation (SDR) method. Results show that the proposed scheme achieves higher effective throughput than fixed or partially adjustable antennas, demonstrating its effectiveness in enhancing covert communication through flexible adjustments.

The 6G mobile communication system leverages wideband extra-large multiple-input multiple-output (XL-MIMO) to achieve ultra-high data rates, which also leads to new security vulnerabilities due to the near-field spherical wavefront characteristics. This work investigates the problem of near-field multi-user covert communication and proposes a joint location estimation (LE) and data transmission (DT) design based on the beam split effect. The proposed method employs a time delay phase shifters (TD-PS) precoding architecture to generate focusable beams at controllable locations across different subcarriers, enabling simultaneous localization of multiple users. Subsequently, beam split is suppressed to support covert communication involving a legitimate user, Bob, and multiple wardens, Willies. In addition, an optimization problem is formulated to maximize the effective covert rate (ECR) under both non-colluding and colluding detection strategies, and a two-stage optimization approach is used to solve the non-convex problem. Numerical results show that the non-colluding detection strategy achieves a higher ECR compared to the colluding one, and demonstrate the effectiveness of the proposed design in balancing covertness and transmission efficiency.

Cognitive Radio (CR) and Energy Harvesting (EH) techniques have offered insights to mitigate issues related to inefficient spectrum utilization and limited energy storage capacity. In Cognitive Radio Networks, security threats, particularly from eavesdroppers, may result in information leakage. This study focuses on enhancing the Physical Layer Security (PLS) of multi-users with EH by employing cooperative jamming via a Autonomous Aerial Vehicle (AAV) to maximize the secure communication rate. In the AAV-assisted EH-CR system, Secondary Users (SUs) can utilize the licensed spectrum band occupied by a Primary User (PU) if the cooperative jamming power from SUs to the PU remains below a certain threshold. SUs can harvest and use Radio Frequency (RF) energy from the Primary Transmitter (PT). The AAV jammer disrupts the eavesdropper by transmitting jamming signals, thereby minimizing stolen information to optimize long-term secure communication performance. The paper formulates the problem of maximizing the average secure communication rate while considering system constraints and jointly optimizes the AAV trajectory, transmission power, and EH coefficient. As the problem is non-convex, it is reformulated as a Markov Decision Process (MDP). The paper employs the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to address the problem, introduces counterfactual baselines to tackle the credit assignment problem in centralized learning, and integrates the Long Short-Term Memory (LSTM) network to enhance the learning capability of sequential sample data, thereby improving the training efficiency and effectiveness of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method in maximizing the system's secure communication rate.

In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.

Cognitive Industrial Internet of Things (CIIoT) permits Secondary Users (SUs) to use the spectrum bands owned by Primary Users (PUs) opportunistically. However, in the absence of the PUs, the selfish SUs could mislead the normal SUs to leave the spectrum bands by initiating a Primary User Emulation Attack (PUEA). In addition, the application of Energy Harvesting (EH) technology can exacerbate the threat of security. Because the energy cost of initiating a PUEA is offset to some extent by EH technology which can proactively replenish the energy of the selfish nodes. Thus, EH technology can increase the motivation of the selfish SUs to initiate a PUEA. To address the higher motivation of the selfish SUs attacking in CIIoT scenario where the EH technology is applied, in this paper, an EH-PUEA system model is first established to study the security countermeasures in this severe scenario of PUEA problems. Next, a new reward and punishment defense management mechanism is proposed, and then the dynamics of the selfish SUs and the normal SUs in a CIIoT network are studied based on Evolutionary Game Theory (EGT), and the punishment parameter is adjusted according to the dynamics of the selfish SUs to reduce the proportion of the selfish SUs’ group choosing an attack strategy, so as to increase the throughput achieved by the normal SUs’ group. Finally, the simulation results show that the proposed mechanism is superior to the conventional mechanism in terms of throughput achieved by the normal SUs’ group in CIIoT scenario with EH technology applied.

In Cognitive Radio (CR) networks combined with Energy Harvesting (EH) technology, Secondary Users (SUs) are vulnerable to jamming attacks when sensing idle channels. At the same time, they may encounter numerous jamming and eavesdropping attacks during the data transmission phase. This paper examines the scenario in which SUs are susceptible to malicious attacks and energy constraints in both the sensing and transmission phases. We propose a utility function applicable to a single time slot. The blockchain uses Smart Contract (SC) technology to set rewards and punishments for users’ channel selection behavior and adjust mining difficulty. This method combines blockchain with spectrum sensing data fusion, abandons the decision-making mechanism of the traditional Cooperative Spectrum Sensing (CSS) Fusion Center (FC), and adopts a distributed structure to ensure the security and reliability of sensing data fusion. In addition, this paper uses the potential game and the Stackelberg game to study the optimal transmission channel and optimal time slot allocation strategy for SUs under malicious attacks. Considering the possible interference caused by channel switching and the greedy principle of Malicious User (MU), the proposed two-layer game method gradually optimizes the sensing detection probability and secure communication rate with time slot iteration. In order to further improve the secure communication rate, an iterative update formula for transmission power is given to make reasonable use of the remaining energy of each SU at the end of each time slot. Simulation results show that the proposed method is superior to traditional methods in both sensing performance and secure communication rate.

This article proposes an incomplete information Bayesian Stackelberg game, which is adapted to the Cognitive Internet of Vehicles (CIoVs) network to defend against spectrum sensing data falsification (SSDF) attacks from malicious vehicle users (MVUs). Specifically, this article considers the random appearance of MVUs caused by mobility, intelligent SSDF attacks of MVUs, and the different spectrum sensing performances among vehicle users (VUs). In the game, the fusion center (FC) as the leader aims to improve the global detection performance while effectively identifying the identities of different VUs by optimizing the global decision threshold and the reputation threshold. On the other hand, this article models the random appearance of MVUs as a Poisson random process, and the MVUs are the intelligent followers; they optimize the attack probabilities according to the FC's strategies to evade detection and increase the chance of selfish transmission and the damage to the CIoV network. To solve the MVUs' nonconvex optimization problem, this article uses the successive convex approximation (SCA) technique to obtain MVUs' optimal attack probabilities. For the FC, this article proposes the method combining alternating optimization and SCA to solve the nonconvex optimization problem of the FC and obtain its optimal defense strategies. This article also proves the convergence of the proposed method and the existence of the Stackelberg equilibrium (SE). The simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.

The high-speed movement of Vehicle Users (VUs) in Cognitive Internet of Vehicles (CIoV) causes rapid changes in users location and path loss. In the case of imperfect control channels, the influence of high-speed movement increases the probability of error in sending local spectrum sensing decisions by VUs. On the other hand, Malicious Vehicle Users (MVUs) can launch Spectrum Sensing Data Falsification (SSDF) attacks to deteriorate the spectrum sensing decisions, mislead the final spectrum sensing decisions of Collaborative Spectrum Sensing (CSS), and bring serious security problems to the system. In addition, the high-speed movement can increases the concealment of the MVUs. In this paper, we study the scenario of VUs moving at high speeds, and data transmission in an imperfect control channel, and propose a blockchain-based method to defend against massive SSDF attacks in CIoV networks to prevennt independent and cooperative attacks from MVUs. The proposed method combines blockchain with spectrum sensing and spectrum access, abandons the decision-making mechanism of the Fusion Center (FC) in the traditional CSS, adopts distributed decision-making, and uses Prospect Theory (PT) modeling in the decision-making process, effectively improves the correct rate of final spectrum sensing decision in the case of multiple attacks. The local spectrum sensing decisions of VUs are packaged into blocks and uploaded after the final decision to achieve more accurate and secure spectrum sensing, and then identify MVUs by the reputation value. In addition, a smart contract that changes the mining difficulty of VUs based on their reputation values is proposed. It makes the mining difficulty of MVUs more difficult and effectively limits MVUs' access to the spectrum band. The final simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.

Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.

Internet of Things (IoT) allows the connectivity of smart devices embedded with sensors, but with the growing problem of overcrowding in unlicensed bands, the data exchange in the network is severely disrupted. Besides, because Cognitive Radio IoT (CR-IoT) networks are composed of many small sensor devices, there is a great need for energy utilization efficiency. Building a new kind of Energy Harvesting enabled Cognitive Radio IoT (EH-CR-IoT) networks by applying EH technology and CR functions to IoT becomes an existing technical solution that can better solve problems such as scarce spectrum resources and valuable energy resources. In order to efficiently and reasonably manage resources such as energy and spectrum for EH-CR-IoT networks, Supermodular Game (SG) theory based resource allocation methods are proposed for both perfect spectrum sensing and imperfect spectrum sensing. The proposed methods first model the resource allocation problems in EH-CR-IoT networks as Bertrand game competition models, then design the utility functions of the consumers and the entire networks in terms of network pricing, next prove that the proposed Bertrand game competition models strictly comply with the theory of SG and the function solutions are Nonlinear Optimization Problems (NOP), after that the Nash Equilibrium (NE) solutions and the optimal network utility are obtained, finally simulation results are presented and prove the validity and the superiority of the proposed methods. Compared with conventional game methods, the proposed methods can better improve the network resource utilization and network benefits.

能量采集(Energy Harvesting,EH)和认知无线电(Cognitive Radio,CR)技术的组合可为物联网设备提供持续的能量,并有效地提高物联网系统的频谱效率.然而,在衬底模式下的认知物联网(Cognitive Radio IoT,CIoT)系统中,物联网设备之间的无线通信常常遭受窃听攻击.针对存在多窃听者条件下的CIoT系统无线通信场景,以保密速率作为系统保密性能指标.为解决所提的资源分配问题,将长短期记忆网络(Long-Term Memory Network,LSTM)、生成对抗网络(Generative Adversarial Networks,GAN)和深度强化学习(Deep Reinforcement Learning,DRL)算法相结合,设计一种联合能量采集时间和传输功率分配方案.数值仿真表明,与其他基准算法相比,所提方法能够有效地提高系统保密性能.

物联网通过采用认知无线电的动态频谱共享机制提高了频谱利用率,然而认知物联网(Cognitive Internet of Things,CIoT)容易受到多种攻击,包括干扰攻击和窃听攻击.首先,基于联盟博弈考虑一个合作模型,其中合法用户合作传输以提高信干噪比(Signal to Interference plus Noise Ratio,SINR),而干扰机合作以提高接收信号强度(Jammer Received Signal Strength,JRSS),窃听机旨在降低系统的保密速率.其次,基于演化博弈论研究了CIoT网络中合法用户和攻击者的动态特性,利用能量采集(Energy Harvesting,EH)技术提高用户的发射功率以提高SINR,从而提高用户的合作水平.此外,通过设置协作干扰节点劣化窃听信道以提高系统的保密速率.仿真结果表明,所提方法在应对干扰攻击和窃听攻击问题上是有效的,且在SINR和保密速率方面优于传统方法.

随着无线终端的大规模普及,用户设备(User Equipment,UE)对无线网络的内容分发服务提出了更高的要求.提出了利用设备对设备(Device to Device,D2D)通信技术进行协作中继传输,使得任何UE都可作为潜在中继节点,并且令中继节点为其他UE中继数据,可以提升整体网络的内容分发质量.为弥补UE作为中继节点产生的能耗,采用能量采集(Energy Harvesting,EH)的激励机制,将携能信号作为奖励发送至UE,以提高UE为其他用户中继数据的意愿.同时,为解决中继选择问题,提出了基于联盟博弈方法,对UE和中继节点的合作行为进行分析,为UE选取最优的中继节点,以获取最优的内容分发服务.仿真结果表明,所提方法与贪婪搜索算法相比,可以更大程度地提高系统的吞吐量.

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system's performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU's spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by 26.3% and the proposed method makes the SUs allocation more fair.

This letter investigates the problem for Spectrum Sensing Data Falsification (SSDF) attacks in the Cognitive Internet of Vehicles (CIoV) network. The high-speed movement of Vehicle Users (VUs) leads to rapid changes in Channel State Information (CSI) and location. This leads to unstable detection probabilities and unstable probabilities of reporting errors. These unstable probabilities increase the error rate of traditional methods to identify VUs as Malicious Vehicle Users (MVUs). And high-speed movement makes it difficult to detect MVUs, which may result in massive MVUs’ attacks. To address the above problems, this letter establishes the Cooperative Spectrum Sensing (CSS) and spectrum access process under a Directed Acyclic Graph (DAG) blockchain framework, models MVUs’ attack strategy selection which is determined by revenue as an evolutionary game, and proposes a smart contract that changes the mining difficulty of VUs based on the correctness of local spectrum sensing decisions to influence VUs’ revenue. Finally, the simulation results verify the theoretical analysis and prove that the proposed method is superior to the traditional method.