Vehicular twin networks (VTN) create virtual agents of vehicular entities through digital twin (DT) technology, replacing physical counterparts in connecting and exchanging traffic information in cyberspace, overcoming physical range constraints and extending information sources for enhanced vehicular decision support. However, the inherent openness of VTN renders communication between DTs, vulnerable to security threats, such as tampering and impersonation, especially in scenarios where DTs are distributed across multiple cloud domains. These issues result in erroneous decisions to threaten vehicular safety because DTs may receive compromised information. To address these challenges, this article proposes a secure and revocable access authentication scheme in the cross-domain VTN. In the scheme, DTs should be authorized first to obtain identity-bound symmetric functions before joining the VTN, and then perform secure access authentication and key agreement with others based on chameleon hash functions for both intradomain and cross-domain communication. Moreover, a dynamic revocation mechanism is introduced to remove malicious DTs from VTN. Formal verification using the Tamarin tool demonstrates that the proposed scheme achieves diverse security properties. Performance evaluation further shows that the proposed scheme outperforms most related schemes in terms of computational and communication overhead.

Vehicular digital twins (DTs) create virtual representations of physical vehicles, enabling real-time data exchange to enhance intelligence and ensure safe driving. Reducing DT content delivery latency in infrastructure-deficient, sparsely populated areas is crucial. This paper develops a novel Satellite-UAV multi-path content delivery framework for data synchronization in vehicular DT applications. Satellites offer wide coverage but suffer from high latency, while UAVs provide rapid deployment and low-latency communication. The framework leverages these unique characteristics to facilitate simultaneous content downloading through multiple paths, thereby reducing latency. A Stackelberg game model is used to motivate effective resource allocation by UAVs. Given the typically private utility model of DTs, a learning-based algorithm is developed to determine optimal pricing strategies for UAVs. Simulation results demonstrate significant enhancements in UAV utility and reduced DT costs, meeting diverse service requirements.

Despite the rapid growth of electric vehicles (EVs), charging remains a time-consuming issue that requires effective management. An important solution uses digital twin (DT) technology, which acts as a virtual agent for EVs in the digital space. DT can analyze real-time vehicle data to develop optimal charging schedules and reserve charging providers in advance through the vehicular network, leading to more efficient charging processes. However, the vehicular network exposes the automated reservation process of the DT to security attacks. Additionally, there is a risk that the actual charging process may deviate from the scheduled requirements set by the DT, resulting in wasted charging resources. To address these issues, this article proposes a secure and efficient charging reservation scheme based on DT technology. To prevent malicious attacks, we first design a secure and privacy-preserving reservation authentication protocol using the extended Chebyshev chaotic maps, taking into account the computational resources of the EV. Furthermore, we develop a reputation mechanism to evaluate and incentivize the charging behavior of EVs. Formal verification and further discussions are conducted to show diverse security functionalities of the proposed scheme can be achieved. We evaluate that the proposed scheme outperforms existing schemes in terms of computation and communication overheads, while also assessing the impact of EV charging behavior on reputation and charging level.

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines.

With the rapid development and extensive deployment of the fifth-generation wireless system (5G), it has achieved ubiquitous high-speed connectivity and improved overall communication performance. Additionally, as one of the promising technologies for integration beyond 5G, digital twin in cyberspace can interact with the core network, transmit essential information, and further enhance the wireless communication quality of the corresponding mobile device (MD). However, the utilization of millimeter-wave, terahertz band, and ultra-dense network technologies presents urgent challenges for MD in 5G and beyond, particularly in terms of frequent handover authentication with target base stations during faster mobility, which can cause connection interruption and incur malicious attacks. To address such challenges in 5G and beyond, in this paper, we propose a secure and efficient handover authentication scheme by utilizing digital twin. Acting as an intelligent intermediate, the authorized digital twin can handle computations and assist the corresponding MD in performing secure mutual authentication and key negotiation in advance before attaching the target base stations in both intra-domain and inter-domain scenarios. In addition, we provide the formal verification based on BAN logic, RoR model, and ProVerif, and informal analysis to demonstrate that the proposed scheme can offer diverse security functionality. Performance evaluation shows that the proposed scheme outperforms most related schemes in terms of signaling, computation, and communication overheads.

A Digital Twin (DT) is a software agent of a physical entity in virtual space, transcending the limitations imposed by physical constraints to enable intelligent services. The emergence of the Internet of Digital Twins (IoDT), by connecting DTs as a network, provides a reliable solution for streamlining the sharing of extensive real-time data among DTs. However, ensuring the security and efficiency of highly sensitive DT data remains a significant challenge in the distributed and dynamic IoDT storage environment. In this paper, we propose a comprehensive scheme that integrates blockchain, Distributed Hash Table (DHT), and Attribute-Based Encryption (ABE) to establish an IoDT data-sharing system. First, we introduce an ABE scheme to achieve data confidentiality and fine-grained access control. By implementing a key generation protocol, we address key escrow concerns in ABE. Second, to ensure bidirectional data confidentiality in data sharing without revealing the identities of DTs, the DHT, and blockchain are jointly applied to achieve resource registration and discovery. Through an XOR mapping operation mechanism, DT data are distributed across different DHT network nodes, reducing DT nodes’ storage consumption and effectively addressing the scalability issue of IoDT storage. Finally, we provide performance evaluations to demonstrate the reliability and efficiency of our proposed scheme. Our scheme strikes a balance between efficiency and security, exhibiting efficient performance in IoDT data sharing compared to other ABE and blockchain schemes.

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink’s network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines.

In this paper, to secure the communication between autonomous vehicle and its digital representative in the vehicular digital twin system, we propose a GAN augmentation-based continuous authentication scheme. Specifically, in the proposed scheme, we first introduce a data augmentation technique based Generative Adversarial Network (GAN) that provides the augmentation of raw data from vehicle sensors. We then present the efficient authentication: 1) We train a Convolutional Neural Network (CNN) using raw and augmented data; 2) Deep features are extracted through a combination of Principal Component Analysis (PCA) and CNN; 3) We train the OC-SVM classifier during the registration to ensure the legality of vehicle in the authentication phase. Performance evaluations via extensive simulations demonstrate the efficiency and effectiveness of the proposed scheme in terms of GAN loss and accuracy.

In recent years, 5G-V2X has promoted the advancement of autonomous vehicles, enabling the latter to obtain more information via 5G networks. However, fast-moving vehicles have to perform frequent handover authentication with base stations in vulnerable wireless channels, which can cause access failures and affect smooth driving. The digital twin is the virtual agent in cyberspace to reliably provide real-time decisions and added-value services to improve the quality of communication for vehicles by analyzing raw data and interacting with the 5G core network. Based on the capabilities of digital twin, in this paper, we propose digital twin-assisted handover authentication scheme that uses the digital twin as the bridge to exchange necessary parameters in 5G-V2X, thereby intelligently assisting in completing mutual authentication and key negotiation between the vehicle and the target base station in advance and reducing the complexity of the handover process. Furthermore, the security and performance analysis demonstrates that our proposed scheme is secure and efficient.

Federated learning (FL) is capable of using the local data sets from large-scale nodes for distributed model training. In FL tasks, training and updating are usually repeated ping-pong processes, in that the model training process between devices (workers) and task publisher (TP) needs to be repeated for multiple rounds towards the global model convergence. However, a worker is typically selfish to save its local resource, and even with an incentive mechanism in place at the beginning of model training, a worker may not be honest to participate in all training rounds, leading to poor performance in global model convergence. To enable long-term cooperation in FL, however, has rarely been considered in the existing literature which motivates our work. In this paper, the multi-round FL is modeled as a dynamic repeated game. To exploit the long-term cooperation gain, a general trigger strategy is deployed as the punishment for free-riding and the Nash equilibrium (NE) of the repeated game is derived. Based on the game theoretic analysis, we develop a NE-driven incentive mechanism to guide the TP selects the most effective wages to motivate workers towards long-term cooperation and avoid midway free-riding. Simulation results show the effectiveness of our proposal.