Game-theoretic denial-of-service (GDoS) attacks exploit rational miners' incentives to degrade the throughput and security of proof-of-work (PoW) blockchains, even when the attacker controls less than 20% of the total hash power. Existing defenses commonly rely on protocol modifications, which risk hard forks and destabilize the system. This paper presents the first rational, protocol-preserving defense against GDoS attacks. We formalize GDoS by unifying selfish-mining-based and blockchain-based denial-of-service variants under a common definition, and establish its theoretical foundation through a dynamic game model with a subgame perfect Nash equilibrium (SPNE). Unlike prior protocol-level defenses, our strategy maintains consensus integrity without introducing any changes to PoW. We propose a cooperative hash-power hopping mechanism in which miners temporarily reallocate hash power to larger pools when under attack to preserve expected payoffs and suppress attacker incentives. To quantify miner utilities under different strategies, we develop a combined game-theoretic and Markov-chain analytical framework and derive closed-form critical profitability thresholds. Simulations calibrated to real-world Bitcoin hash-power distributions show that the proposed strategy reduces attacker revenue gains by more than 20% and prevents throughput degradation across the entire attack range. These results demonstrate that rational, incentive-compatible cooperation can effectively strengthen PoW blockchains against emerging strategic threats.

With the increasing demand for network resource management, traditional centralized management approaches face challenges such as single point of failure, excessive permissions, and privacy breaches when sharing resources across multiple domains. To address these issues, this paper proposes a blockchain-based trustworthy management solution for network resources. The proposed solution ensures data privacy and security through a decentralized blockchain mechanism, while providing fair and transparent resource allocation. The system design encompasses privacy protection, data immutability, authorized access control, as well as fairness and incentive compatibility in resource distribution. Analysis indicates that this solution enables effective and equitable resource management in the face of DDoS attacks, thereby providing an innovative pathway for building a secure and scalable network management system.

With the dramatic increase in the number of low Earth orbit (LEO) satellites, satellite handover strategies have become a research hot topic. However, most existing schemes largely ignore the problem of users’ personalized needs. To address this issue, we propose a handover scheme that combines user dynamic demands with multi-agent reinforcement learning. Specifically, we first construct a user dynamic preference matrix to extract user preference factors. Then, we use the entropy method combined with confidence intervals to dynamically calculate the attribute weights that satellites provide to users. This approach effectively eliminates the uncertainty of weight allocation and the influence of subjective assignment. By weighted fusion of user preferences and attribute weights, we establish a multi-attribute optimization model. Additionally, we design a distributed multi-agent QMIX (Q-mixing) reinforcement learning framework. In this framework, each user is regarded as a separate agent. The multi-attribute optimization model is used as the reward function for reinforcement learning, and collaborative optimization of handover strategies is achieved through asynchronous learning and a global reward mechanism. Simulation results show that our scheme achieves user satisfaction exceeding 60% for approximately 80% of users. It also provides higher signal quality and lower delay, while avoiding unnecessary handovers.

Fingerprint recognition technology is widely used in various terminal devices and serves as a powerful and effective method for authentication. Existing research relies on centralized training models based on datasets, overlooking the privacy and heterogeneity of the data itself, resulting in the leakage of user information and decreased recognition accuracy. In order to solve the problem of data security and privacy protection, this paper proposes a federated learning-based architecture called FedFR(Federated Learning-Fingerprint Recognition). The parameters from each endpoint are iteratively aggregated through federated learning to improve the performance of the global model under privacy constraints. Moreover, to solve the client-side unfairness issue in traditional federated learning caused by randomly selecting aggregation weights, a client selection method based on reservoir sampling is proposed, increasing the diversity of data distribution. Using the real-world databses, the effectiveness of FedFR is compared and analyzed through simulation experiments. The results show that FedFR exhibits good performance in terms of privacy protection levels, evaluation accuracy, and scalability. Distinct from traditional fingerprint recognition algorithms, FedFR improves the security and scalability of the model from the data source, providing a reference for the application of federated learning in biometric technology.

Cyber–physical system (CPS) provides a foundation for the Industrial Internet of Things (IIoT) that interconnects all types of devices. The integration of CPS with IIoT generates the large volumes of data forcing the development of artificial intelligence (AI) to extract information more precisely. Nevertheless, the increasing volume/variety of data traffic and the ever-growing number of IIoT devices bring great challenges for the host-centric communication model of the current Internet. In this work, we present a novel information-centric networking (ICN)-based system model in CPS, which enables processing data from IIoT devices closer to the edge as opposed to a content provider. Based on this ICN system model, we propose an edge-assisted authentication scheme in CPS, aiming to protect the system from unauthorized access and reduce workload for resource-constrained devices. The main features of our scheme include a delegation model of security operations and session handshake procedures through edge routers, addressing the rising challenges in managing and securing IIoT devices in the ICN. We formally prove the security of our scheme and conduct performance analysis to show its practicality.

This article studies the distributed fault-tolerant bipartite output synchronization problem of discrete-time linear multiagent systems (MASs) with process faults under a general directed signed graph. The reference signal is generated by an autonomous exosystem, which can also be seen as a leader. All followers are divided into two subgroups with antagonistic interactions, and the followers in each subgroup are cooperative. We aim to solve the bipartite fault-tolerant control (FTC) problem via the output regulation theory such that bipartite output synchronization can be achieved in the presence of process faults, that is, the outputs of followers with different subgroups can approach the output of exosystem with the same magnitude and the opposite sign regardless of process faults. To estimate the states and the faults of each follower, a simultaneous state and fault estimator based on the neighboring signed output estimation error and the standard discrete-time algebraic Riccati equation (ARE) is designed. Besides, a new exosystem observer with two classes of convergence conditions relying on the respective solutions of standard and modified AREs is provided. All eigenvalues of the exosystem matrix can lie completely outside the unit circle. Based on these estimations, we present a distributed fault-tolerant output feedback controller, which can overcome the no-loops constraint. Finally, simulation results are given to demonstrate the analytic results.

Abstract With the purpose of ensuring secure communication through wireless environments, authenticated key agreement protocols with user anonymity are widely investigated. Inspired by the semi‐group property of Chebyshev maps and multiple servers in the network environment, Tsai et al. proposed a novel chaotic maps‐based anonymous authenticated key agreement protocol based on multiserver architecture. Unfortunately, we observe that the Tsai et al. protocol falls to key‐compromise impersonation attack, which opens the door for an attacker to launch an offline password‐guessing attack. Moreover, the Tsai et al. protocol also unfortunately violates the session key security. Elaborating on the security of chaotic maps‐based authenticated key agreement, we present an enhanced protocol employing biometrics that attempts to repair the security pitfalls found in Tsai et al. Security analysis shows that the enhanced protocol satisfies more security attributes while retaining the merits of the original protocol. We also present a formal proof of the enhanced protocol with the Burrows–Abadi–Needham logic. The performance of our protocol is evaluated with its predecessor protocols, and the comparative results show that it outperforms the predecessor protocols in terms of better trade‐off between desirable security attributes and computational overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract With the greatest advancement of information technology, mobile communication has become more widespread and prevalent. When a mobile user intends to enjoy the services offered by a remote server, he needs to be authenticated before constructing a session key with the corresponding server. Numerous authentication schemes have been provided with the purpose of validating the legitimacy of a mobile user. Recently, Xieet al.presented a modified two‐factor authenticated key exchange to eliminate the security flaws of Chenet al.Xieet al.claimed that the enhanced design was more secure than the design of Chenet al.Unfortunately, we identified that the proposed scheme by Xieet al.was insecure against user impersonation, insider and trace attacks and did fail to provide verification in login phase. To enhance the security and efficiency, we then proposed an anonymous authenticated key exchange scheme for mobile client‐server environment. We demonstrated that the proposed scheme was immune to many attacks including attacks observed in the scheme of Xieet al.We also use a formal proof, namely Burrows–Abadi–Needham logic, to analyze the proposed scheme. In addition, the proposed scheme possesses a lower computation overheads than the other related schemes. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid development of computer networks, multi-server architecture has attracted much attention in many network environments. Moreover, in order to achieve non-repudiation which both passwords and cryptographic keys cannot provide, several password authentication schemes combining a user's biometrics for multi-server environments have been proposed in the past. In 2014, Chuang et al. presented a biometrics-based multi-server authenticated key agreement scheme and declared that their scheme was efficient and secure. Later, Mishra et al. commented that the scheme by Chuang et al. was susceptible to stolen smart card, impersonation and denial of service attacks. To conquer these weaknesses, Mishra et al. presented an efficient biometrics-based multi-server authenticated key agreement scheme using hash functions. However, we prove that the scheme by Mishra et al. is insecure against forgery, server masquerading and lacks perfect forward secrecy. The focus of this paper is to present a robust biometrics and public-key techniques-based authentication scheme, which is a significant enhancement to the scheme recently proposed by Mishra et al. The highlight of our scheme is that it not only conquers the flaws but also is efficient compared with other related authenticated key agreement schemes. Copyright © 2015John Wiley & Sons, Ltd.