The issue of association privacy leakage has become increasingly critical during data release and usage. However, traditional privacy protection techniques often struggle to address privacy leakage resulting from implicit associations within the data. In this paper, we propose a novel mechanism based on Cobweb Privacy to safeguard association privacy more comprehensively. Firstly, we design the concept of ϵ-Cobweb Privacy (ϵ-CP) specifically to address association privacy leakage. This concept extends the traditional notion of differential privacy by incorporating associated prior knowledge, thereby offering more effective and comprehensive protection of association privacy. We further demonstrate its privacy guarantees through the theoretical analysis of the relationship between ϵ-CP, differential privacy, and pufferfish privacy. Secondly, we quantify the privacy leakage problem mathematically and examine the utility privacy trade off under various priors. Additionally, we present a universal framework for association privacy protection in data aggregation scenarios using the ϵ-CP mechanism. Finally, this framework is integrated with three different noise addition methods and compared against mechanisms based on differential privacy and pufferfish privacy, and its utility is validated through experiments on both non-temporal and temporal real-world datasets. The results show that ϵ-CP provides distinct advantages in the utility privacy trade-off.

Reliable encrypted traffic classification is crucial for fine-grained and efficient network security management, enabling accurate user behavior recognition and cybercrime forensics. While AI-based methods can automatically extract subtle features from traffic data, existing approaches often fail to effectively capture and integrate features across different levels of traffic granularity, namely the byte, packet and flow levels. Current graph-based methods heavily rely on manual feature engineering to construct global IP-based graphs, overlooking critical packet-level temporal features and byte-level raw information. Focusing on only one or two levels of traffic granularity is unreliable and insufficient, ultimately compromising model accuracy and robustness. To address these limitations, we propose BPF-DAG, a byte-packet-flow feature fusion framework based on dynamic attributed graphs, for reliable encrypted traffic classification. To the best of our knowledge, this is the first method that integrates temporal packet relations into flow interaction patterns while directly leveraging raw byte-level data. Specifically, we introduce a multi-granularity feature fusion strategy that dynamically updates an IP-based graph by iteratively assigning edge attributes derived from evolving flow representations. During the joint training of the Transformer and the graph neural network, temporal representations are learned from raw packet sequences and reflected in edge attributes dynamically for further message aggregation. Experiments on the ISCX VPN-nonVPN, Tor-nonTor, MIRAGE-2019 and MIRAGE-2024 datasets show that BPF-DAG outperforms recent state-of-the-art methods in terms of classification performance.

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet of Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication; an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks; and an adaptive reputation update strategy, which is proposed for evaluating the nodes’ behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information.

Due to the integration of cloud computing in Internet of Things (IoT) systems, an increasing volume of IoT data is encrypted and uploaded to cloud servers. Attribute-Based Keyword Search (ABKS) schemes enable authorized users to access the encrypted IoT data, however, existing schemes suffer from sensitive information exposure (caused by publicly visible access policies) and low efficiency in dynamic multi-party sharing. To alleviate these issues, this paper develops an efficient attribute-based searchable proxy re-encryption scheme with policy hiding for IoT data sharing. Specifically, the scheme employs a layered encryption architecture where symmetric encryption (e.g., AES) protects massive IoT data, and Attribute-Based Encryption (ABE) secures the symmetric key. Such design ensures that policy updates for dynamic multi-party sharing are implemented by updating the ABE ciphertext using proxy re-encryption keys, eliminating the need to re-encrypt bulk IoT data. Meanwhile, an AND-gate structure for multi-valued attributes is introduced to enforce access policies while ensuring policy hiding and protecting data owners’ sensitive information. Additionally, the proposed scheme achieves constant overhead (key/ciphertext sizes are independent of attributes), supports fast keyword search, and enables low-latency data retrieval in IoT systems. Rigorous security analysis and comparative evaluations with closely relevant schemes validate the security and practicality of our scheme.

Space-air-ground integrated network (SAGIN), which integrates satellite systems, aerial networks, and terrestrial communications, offers ubiquitous coverage for a multitude of applications. Nevertheless, the highly dynamic and open nature of SAGIN increases the network’s vulnerability. Hence, zero-trust security, operating on the principle of “never trust, always verify”, holds the significant potential of securing SAGIN. However, implementing zero-trust SAGIN in practice presents three primary challenges: 1) understanding massive unstructured threat information across diverse domains, 2) performing adaptive security assessments, and 3) making in-depth security decisions. This motivates us to propose SAG-Attack and LLM-SA to enhance zero-trust SAGIN. SAG-Attack serves as a simulator that aims to mimic various attacks in SAGIN. Our LLM-SA is a novel situation awareness method that explores the multiple agents of large language model (LLM). Specifically, the output logs of SAG-Attack will be fed into LLM-SA, and LLM-SA fuses vast amounts of heterogeneous threat information from various domains, thus tackling the first challenge. Then, our LLM-SA relies on multiple LLM-based agents to perform adaptive security assessments, utilizing the chain-of-thought capabilities of LLMs to automatically generate in-depth defense strategies, thereby addressing the second and third challenges. Experiments on five benchmarks demonstrate the superiority of the proposed SAG-Attack and LLM-SA. Notably, our method based on open-sourced Llama3-8B even outperforms ChatGPT-4 under the same setting, despite involving significantly fewer parameters. To foster further research in this area, we will release our platform to the community, facilitating the advancement of zero-trust SAGIN.

Group-based authentication allows users within a single domain and group to access networks without repeating an individual authentication instance, greatly reducing the energy consumption of low-resource mobile devices in IoT and M2M communications. Nevertheless, in the case of the upcoming 6G massive communications with an exponentially larger number of connections, the computation and communication overhead of existing approaches on mobile devices are still significant. To this end, we propose GROSS, a novel GRoup-based authentication and key agreement (AKA) protocol that uses a One-time Secure Secret-sharing mechanism for more efficient authentication over massive wireless communications. Specifically, we employ a lightweight cryptographic operation for the above one-time secret sharing. The proposed GROSS significantly reduces both computation and communication overhead by consistently maintaining the validity of credentials for group-based authentication, thus enabling efficient verification of device legitimacy within a group. We also implement a simulation platform on JAVA for energy consumption evaluations for massive wireless communications. Our platform facilitates the flexible configuration of various energy components for authentication and supports up to million-level wireless connections. We conduct extensive experiments to show the effectiveness of our proposed GROSS.

Existing physical-layer authentication (PLA) schemes of tag superimposed on message signals (TSM) can achieve high authentication accuracy at the cost of increased latency and reduced communication performance. The schemes of tag superimposed on pilot signals (TSP) achieve desirable communication performance and low latency, but low randomness of the tag results in lower security. To further improve both security and communication performance, we propose a pseudo random watermark hopping-based PLA scheme in this article. The proposed scheme generates a pseudo-random sequence and designs a watermark hopping mechanism, which superimposes a carefully designed tag on the pilot or message signals accordingly. The proposed scheme enhances the security by utilizing the randomness from both tag generation and watermark hopping mechanism. Meanwhile, it decreases the authentication latency and improves the communication performance by superimposing the tag on the pilot signals without the message recovery process before authentication. The theoretical and experimental results demonstrate that the proposed scheme decreases the bit error rate (BER) and outage probability as well as increases the achievable rate of the system compared with the TSM scheme with the same key equivocation. Moreover, the security performance of our scheme is significantly improved compared with both TSM and TSP schemes.

Reconfigurable intelligent surface (RIS) has emerged as a highly promising infrastructure benefiting from its capability to manipulate the propagation environment and facilitate efficient aggregation of wireless transmission signals. However, a major challenge in these systems is the significant overhead incurred by the acquisition of channel state information. This paper proposes a pre-designed beam-based transmission protocol that aims to reduce the burden of pilot overhead by designing a RIS reflective pattern codebook as an alternative to channel estimation (CE). We adopt an electromagnetic-compliant RIS model and develop a theoretical approximate expression for the channel gain, incorporating the impact of location mismatch. This approximation facilitates the construction of a location-based reflective pattern codebook, wherein the chosen locations linked with the codewords represent optimal location sampling points derived from theoretical results. By utilizing the constructed codebook, our proposed transmission protocol enables the system to search reflective patterns instead of real-time optimization. To validate our approach, extensive numerical simulations are conducted. The results demonstrate the accuracy of the approximate channel gain expression and highlight the superior coverage achieved by the proposed location-based reflective pattern codebook as well as the achievable spectral efficiency of our transmission protocol.

Given the frequently changing and potentially unreliable environment, the seamless handover authentication is essential to achieve zero-trust Internet of Vehicles (IoV) network with dramatically enhanced communication and transportation safety. The traditional centralized handover authentication schemes may suffer from the excessive latency and situation agnostic limitation, leading to potential interruption of critical services for fast moving vehicles. To overcome the above challenges, this paper proposes a novel decentralized edge collaboration-based handover authentication scheme with the assistance of blockchain for providing continuous protections in zero-trust IoV. A distributed learning process is designed by involving multiple authentication cooperators (ACs) to collect device/location-related features of vehicles at network edge and then to verify their identities. During the movement of vehicles, the access point (AP) could select new ACs by transferring the security information from existing ACs to the new members for seamless handover authentication. A situation-aware AC selection and update algorithm is proposed for maximizing handover authentication accuracy. Moreover, a hierarchical blockchain-assisted security information transfer and reputation management mechanism is designed for reliable collaboration and efficient management in zero-trust IoV. Compared with the existing schemes, our results characterize the outperformance of the proposed scheme in authentication accuracy and time cost of handover.

Continuous authentication is essential to realize the new zero-trust based security provisioning. Conventional authentication techniques often rely on security keys, credentials, or device fingerprints, which may suffer from either high network overhead or low reliability in highly dynamic environments. To concurrently overcome these challenges, we jointly design the multi-dimensional multiple access and lightweight continuous authentication (MDMA-LCA) to explore multiple domains of the users' access channels for both communication and security enhancement. The access time frame, subchannel, and power allocation of multiple users are formulated as a joint optimization problem to maximize the achievable sum rate (ASR) of the users while continuously authenticating their identities assisted by the non-orthogonal multiple access (NOMA). The proposed scheme achieves lightweight continuous authentication by prearranging the access time sequences of multiple users and by verifying them directly and simultaneously at the base station (BS). Then, the joint optimization problem is decomposed and transferred to a maximum flow problem in a designed graph, and a joint MDMA-LCA algorithm is developed. Simulation results demonstrate that, compared with several existing schemes, the proposed scheme achieves an ASR gain while guaranteeing the continuous authentication of the users.

While edge collaborations play more and more important roles in the sixth-generation (6G) network, the authentication among devices for trusted collaborations is more challenging. The existing authentication mechanisms may suffer from the long latency and high computation overhead in such application scenarios, especially when the collaboration group is large. In this paper, a lightweight group authentication scheme based on multi-dimensional historical information is proposed and implemented in a specific federated learning-based collaborative outdoor localization scenario. To further illustrate, the multi-dimensional historical information consists of the learning parameters and environment sensing data collected by lidar from the latest round of collaboration. Then, we design a key generation strategy based on the historical information and develop a group authentication protocol. In the proposed scheme, every device in the group can identify the others at once based on their broadcasting keys, which will be renewed automatically before every round of collaboration. Hence, the proposed scheme achieves lightweight group authentication and high security. Both implementation and simulation results demonstrate the validity and superior performance of the proposed scheme compared with the existing schemes.

In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with Soil Moisture Active Passive (SMAP) data and found a certain deviation in the CYGNSS ”young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the ”ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS.

Existing device authentication techniques may suffer from heavy communication, computation, and storage overhead for identifying a growing number of devices in collaborations. This paper proposes a novel group authentication (GA) method for decentralized edge collaboration by exploiting the historical collaboration process information, i.e., the distributed learning parameters and results from the previous round of collaboration. Two strategies are developed to generate tokens locally at the edge devices’ side for mutual authentication, named random token generation (R-TG) and privacy-preserving token generation (PP-TG). Specifically, the R-TG strategy randomly selects several historical learning parameters as tokens, while the PP-TG strategy designs a one-way function to defend against privacy leakage by concealing the historical information. A GA protocol is proposed, where each device simultaneously authenticates the others in the same group by repeating the learning process using their tokens. If the process converges to an expected result, all the devices are authenticated as legitimate group members at once. The proposed scheme provides a lightweight flexible solution without pre-generating and distributing any keys/secrets operating on top of a standardized security protocol, and protects the collaboration continuously. The simulation results demonstrate the viability of our scheme and its superior performance compared to several benchmark schemes.

Conventional centralized authentication techniques based on both digital cryptography and physical-layer attributes are prone to single-point failure due to either compromised digital security keys or an abrupt change in the physical communication environment. Although these particular challenges could be mitigated by the joint use of decentralized authentication and physical-layer attributes, such schemes often exhibit unpredictable performance. Simultaneously, the necessary involvement of multiple parties and the imperfect observation of the physical communication environment can also significantly increase the latency and computational complexity. As a remedy, a decentralized authentication scheme is proposed in this paper to achieveGuaranteed Authentication Level at Minimized Complexity(GALAMC) based on the intelligent use of distributed collaboration and available distributive physical-layer attributes. Specifically, we aim for minimizing the complexity of the proposed collaborative authentication process by harnessing the minimum number of collaborative nodes and the selected authentication attributes at each node across the different environments while guaranteeing the required authentication level. The related physical-layer authentication scheme is implemented at each collaborative node where different physical-layer attributes can be selected based on their usefulness which is time-varying. The simulation results demonstrate that our scheme maintains the target level of authentication and it is more immune to sudden environmental changes than the conventional centralized physical-layer authentication scheme. It can also be observed that our proposed scheme can adaptively select the minimum number of collaborative nodes for adaptively minimizing the computational cost.

Although cross-polarized synthetic aperture radar (SAR) images play a crucial role in wind speed retrieval under extreme weather conditions, the retrieved wind speeds are susceptible to thermal noise. However, there is still a lack of research on how thermal noise affects the accuracy of wind speed retrieval. To address this issue, this paper proposes a strategy to quantitatively examine the impact of thermal noise on wind speed retrieval, using 910 Sentinel-1 cross-polarized SAR images acquired in Extra-Wide Swath (EW) mode. The thermal noise and wind speeds of these images range from -35 dB to -22.5 dB and from 5 m/s to 70 m/s, respectively. By considering the wind speed retrieved from dual-polarized SAR as a reference, the study reveals that higher levels of thermal noise result in increased uncertainty in the retrieved wind speeds using cross-polarized SAR images. Additionally, the error of wind speed retrieval from cross-polarized SAR rapidly decreases as wind speed increases, eventually converging to a stable level. Notably, as thermal noise levels decrease to less than -30 dB for wind speeds between 5 m/s and 25 m/s, the root-mean-square error (RMSE) associated with wind speed retrieval through cross-polarized SAR imagery experiences a rapid decline, with a 94% reduction in RMSE, which then stabilizes. This implies that when the thermal noise level falls below -30 dB, wind speed retrieval can be directly conducted using cross-polarized imagery for wind speeds exceeding those typical of a tropical storm, yielding comparable results to dual-polarized imagery. The findings of this study provide valuable insights for advancing wind speed retrieval algorithms and hold significant reference value for the design of the next-generation radar instruments that will incorporate the cross-polarized channel.

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.

The detection of anomalous Internet-of-Things (IoT) sensor data is extremely important in many industrial applications due to the catastrophic consequences of the faulty or unreliable sensor data. Good anomalous data detection performance with high detection efficiency is indeed a dilemma since it is difficult to derive an explicit detection function to characterize the relationships between the anomalous values and the detection indicator. To overcome this difficulty, the location information of the IoT sensors is exploited in this study to characterize and reconstruct the relationships among the sensor data based on a second-order nonlinear polynomial graph filter (NPGF). The analysis of the sensor data reconstruction model is then conducted in the frequency domain based on the 2-D inverse graph Fourier transform (GFT), and the reconstruction error function for the sensor data is analytically derived based on the second-order GFT coefficients. It is shown that the detection efficiency can be greatly improved if the input graph signal is designed to be bandlimited. The anomalous sensor data detection is then conducted in the frequency domain as high-frequency components are more sensitive to the deviation values. An NPGF-based frequency-domain algorithm is proposed for the anomalous sensor data detection, which is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the detection performance and efficiency improvement of the proposed algorithm in anomaly detection.

Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost.

Detecting the existence of anomaly and localizing the faulty sensors in the Internet-of-Things (IoT) systems are extremely critical, since the incorrect data could lead to catastrophic consequences in many vertical industry applications. The difficulties of such problems come from deriving an explicit error function for each sensor in IoT, and the data continuity in the temporal domain would also be seriously challenged. To overcome these difficulties, the irregular spatial information of the IoT sensors is utilized by constructing an adjacency matrix using the distances among different sensors, and the nonlinear polynomial graph filter (NPGF) is employed to characterize the relationships among the collected sensor data. The NPGF provides a more accurate model for reconstructing the sensor data by taking the data nonlinear relationships into account. The error functions at each sensor for newly detection data are theoretically derived, and it is demonstrated that the error at the anomalous sensor performs differently from that of the normal sensors if the adjacency matrix is designed appropriately. The proposed NPGF-based algorithm is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the superior performance of our scheme in both anomaly detection and faulty sensor localization when compared with existing algorithms, such as the graph frequency algorithm and oversampling PCA (OS-PCA) method, especially for the case of small sensor data deviations.

Conventional authentication mechanisms routinely used for validating communication devices are facing significant challenges. This is mainly due to their reliance on both `spoofable' digital credentials and static binary characteristic, and inevitable misdetection in physical layer authentication using time-varying attributes, leading to the cascading risks of security and trust. To circumvent these impediments, we develop an adaptive trust management based soft authentication and progressive authorization scheme by intelligently exploiting the time-varying communication link-related attribute of the transmitter to improve wireless security. First of all, the trust relationship between the transmitter and receiver is established based on the evaluation of selected physical layer attribute for fast authentication and multiple-level authorization. Through the designed trust model, the transmitter is authorized by the specific level of services/resources corresponding to its trust level, so that soft security is achieved. To dynamically update the trust level of the transmitter, we propose an online conformal prediction-based adaptive trust adjustment algorithm relying on the real-time validation of its attribute estimates at the receiver, thus resulting in progressive authorization. The performance of our scheme is theoretically analyzed in terms of its individual risk and individual satisfaction. Our simulation results demonstrate that the proposed scheme significantly improves the security performance and robustness in time-varying environments, and performs better than the static binary authentication scheme and existing physical layer authentication benchmarker.

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.

We report an investigation of ocean-surface wind speed retrieval from C-band RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) images using a new channel of coright-circular polarization (RR-pol) in compact polarimetry (CP) option. The analysis of simulated RCM quad-polarized CP SAR data and collocated in situ buoy measurements suggests that the RR-pol is much less sensitive to wind directions than the other three polarizations in the CP option. A method is proposed for the RR-pol radar signal as a function of wind speed and incidence angle. We demonstrate that C-band RR-pol has the potential for ocean high wind retrieval, especially for cyclone studies.

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machinelearning as an intelligent process to learn and utilize the complex time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single-dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least mean square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

In this paper, a coordinated multiple-relays-based cooperative communication scheme is proposed to improve the physical-layer security. In order to benefit the relays in forwarding the signals for defending against the eavesdropping attacks, the interactions between the source and the multiple relays are modeled as a single-leader multiple-followers Stackelberg game. The source plays as the leader to coordinate the relays, including the phase of signals forwarded by the relays and the transmit power of the relays, for maximizing the secrecy capacity of the system. An algorithm is developed for the relays to find an optimal price allocation to achieve the fairness among the multiple relays based on the egalitarian welfare solution, and an approximate optimal strategy of source (i.e., phase coordinated vector and power allocation) is studied. The closed-form intercept probability of the proposed scheme is derived. Numerical studies demonstrate that the proposed scheme can greatly improve the utilities of both the source and multiple relays over that resulted from the Nash equilibrium scheme and rand scheme, which means that the relays are more willing to participate in the cooperative communication, and the source can achieve better secure transmission based on the proposed scheme. It is also shown that the proposed scheme performs much better in defending against the eavesdropping attacks than those existing schemes, for example, the single relay selection scheme, the opportunistic relay selection scheme, the optimal relay scheme, and cooperative jamming scheme.

The emergence of sea surface current has a significant impact on electromagnetic (EM) backscattering signals. This may be one of important synthetic aperture radar (SAR) imaging mechanisms of ocean currents. Fractal sea surface ocean wave-current model is derived based on the mechanism of wave current interaction in this paper. A effective EM backscattering model of one dimensional drifting fractal sea surface is presented. numerical results show that both magnitude and direction of ocean current have effects on EM backscattering signals from one dimensional ocean wave-current coupled fractal sea surface. The existence of ocean currents which paralleling to the direction of wave can weaken EM backscattering signal intensity. EM backscattering signal intensity can be strengthened by ocean currents propagating in the opposing direction of wave.

Summary The era of big data is here now, and spectrum resources are increasingly scarce in heterogeneous network environment. The spectrum efficiency and secure transmission of big data are important issues. Cognitive radio has been proposed to address the issue of spectrum efficiency, and is a hot topic in the literatures. In multi‐hop cooperative cognitive radio networks (CCRNs), secondary users need the primary users' authorization to be relays. Most existing centralized route selection schemes ignore the energy allocation, and thus are inefficient. Moreover, the incomplete of information in multi‐hop network leads to many difficulties in cooperation. Inspired by the game theory, a novel strategy is proposed in this paper to defend against insider attacks based on trust. This strategy is denoted as secure routing and resource allocation based on game theory in CCRNs (SRGC). With a reputation updating process and distributed learning algorithm, the proposed strategy can find a ‘best’ route, which is relatively safe for each primary transmitter, and at the same time fully utilizes the spectrum and energy. Using NS2, simulations indicate that SRGC can well fit into CCRNs, improve the network performance and defend against the routing disruption attacks. Compared with other schemes, the SRGC results in a performance with better adaptability to the distributed environment. Moreover, SRGC can maximize the average throughput and minimize the data drop ratios. Copyright © 2015 John Wiley & Sons, Ltd.

In order to fully utilise limited spectrum resources of multiple channels and multiple radios in cognitive radio networks, the authors propose a potential game model for opportunistic spectrum access based on both accurate and inaccurate spectrum state estimation with considering the interference constraints of licensed users. Three algorithms are proposed to achieve equilibrium of the proposed game. First, assuming spectrum sensing results are accurate, a joint strategy fictitious play‐based channel selection algorithm with incomplete information is presented, and it can achieve a pure Nash equilibrium (NE) of the proposed game. Second, in order to make the outcomes of game robust, an evolutionary spectrum access mechanism with complete information is introduced by using evolutionary game theory based on inaccurate spectrum state estimation so that evolutionary stable strategy (ESS) can be achieved. Finally, with incomplete network information, a distributed learning algorithm is proposed to achieve a mixed NE, which is proved to be an ESS. Simulation results show that these algorithms can significantly improve spectrum allocation efficiency while reducing mutual collision.

Using partial observable Markov decision process(POMDP) and game theoretic solutions, we investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs). In order to fully utilize the scarce spectrum resources, we propose two special cases to study the dynamic spectrum access. Firstly, the channel state prediction based on POMDP could reduce the collision of SUs with PUs, Secondly, a potential game(PG) theoretic framework and joint strategy fictitious play(JSFP) have been proposed to determine the access probability of SU, which could reduce the collision with other SUs. It is shown that with the proposed cases, global optimization has been achieved with local information. Specifically, the strategy with two cases mentioned above maximizes the network throughput and minimizes the network collision level. Meanwhile, the JSFP, which works to provides strong guarantees on the resulting asymptotic behavior, is proposed to achieve the global optimum autonomously and rapidly. Simulation results show that the proposed scheme can greatly improve the spectrum efficiency by alleviating mutual collision.