In the Internet of Vehicles (IoV), developing accurate road information models is essential for analyzing perception data gathered from multiple vehicles. However, traditional centralized data-sharing methods can compromise the privacy and security of data providers. federated learning (FL) presents a promising solution as a distributed machine learning approach that balances data privacy protection with efficient utilization by keeping data localized and sharing only model updates. Nevertheless, conventional FL strategies often fail to adequately address differences in resource investment and data quality among participating vehicles while aggregating local training results. This oversight can lead to inequitable model aggregation and distribution, reducing the motivation for vehicles to share their data. This article proposes a reputation evaluation-based, fair, and secure FL scheme for the IoV to address these challenges. In this scheme, the aggregation node utilizes fuzzy comprehensive evaluation to assess the training outcomes of participating vehicles and assigns aggregation weights accordingly. It also calculates reputation values for each vehicle using periodic averaging methods. Subsequently, the node implements differentiated global model compression and distribution based on these reputation scores. Experimental results indicate that the proposed scheme performs comparably to established algorithms while effectively evaluating vehicle reputations. It achieves model compression and equitable distribution, demonstrating an ability to identify and counteract malicious client attacks. Consequently, this approach enhances fairness and security in FL systems designed for the IoV.

In vehicular networks, securing the network relies on analyzing extensive vehicle perception data to create an accurate road information model. Effective data sharing is crucial for this objective. However, traditional centralized data sharing methods jeopardize the privacy and security of data providers. Federated Learning has emerged as a solution to this challenge. Unfortunately, conventional Federated Learning strategies often overlook the differences in resource investments and data quality among participating vehicles, leading to unfair model distribution and diminishing the willingness of vehicles to share data. This paper proposes a two-stage Federated Learning strategy. In the first phase, aggregation nodes employ a fuzzy comprehensive evaluation method to assess the training results of participating vehicles and allocate aggregation weights accordingly. In the second phase, aggregation nodes calculate vehicle reputations using a periodic averaging method and distribute a differentially compressed global model based on these reputation values. Experimental results demonstrate that the proposed strategy achieves performance comparable to existing Federated Learning algorithms while effectively calculating vehicle reputations, ensuring fair distribution, and mitigating malicious clients.

The introduction of satellite Internet resource-slicing technology can efficiently allocate satellite network resources and meet the personalized needs of different users. This article proposes a trust-based satellite Internet resource-slicing access authentication scheme, which solves the efficient and secure access requirements in situations where satellite communication and service resources are relatively limited. The working idea of this article is to provide users with access authentication protocols with different efficiencies through trust as a standard. Firstly, The user’s trust value is calculated by establishing a trust metric model based on Beta function, communication byte fluctuations, and centralized trend measurements. Drawing on the requirements of the security policy function in the resource slicing technology standard, assigning different security policies to users can both improve the fast access ability of high-trust users and reduce the priority of low trust users’ access. After that, based on the results of trust metrics, this paper proposes a two-factor-based no certificate satellite Internet slicing access authentication protocol for users with moderate trust levels. This protocol achieves the ability for users to access slicing services anonymously and efficiently through the use of resource-slicing credentials and managers. Final, this article verify the correctness and security of the protocol. Through communication cost comparison, it is shown that this protocol has fewer costs. Through trust simulation, the effectiveness of the trust scheme is analyzed and compared.

Federated Learning (FL) has emerged as a popular distributed learning framework, enabling participants to collaboratively train models without the need to share raw data. In contrast to traditional machine learning frameworks that require the collection of large volumes of user data for centralized training, FL provides a novel approach that ensures data privacy and addresses the issue of data silos. However, the conventional FL framework faces challenges posed by the Byzantine Generals' Problem, where malicious clients can easily compromise the global learning process. Existing Byzantine solutions in FL are ineffective in defending against such attacks. This paper proposes a novel FL method called FLEvaluate, which assesses client trust based on performance metrics of their models and assigns aggregation weights according to their trust levels. By incorporating trust evaluation and normalization of consequences, this method amplifies or reduces the impact of clients in the global model aggregation process, thereby achieving a robust FL framework. Comparing FLEvaluate with the traditional FL framework FedAvg reveals that not only does FLEvaluate achieve comparable accuracy and precision but it also demonstrates effective defense against Byzantine attacks.

This article studies the integration of laser-beamed wireless power transfer (WPT) into high-altitude platform (HAP)-aided multiaccess edge computing (MEC) systems for the HAP-connected aerial user equipments (AUEs). By discretizing the 3-D coverage space of the HAP, we present a multitier tile grid-based spatial structure to provide the aerial locations in the form of tile grids to AUEs for laser charging. We identify a new privacy vulnerability caused by the openness during the WPT signaling transfer in the presence of a terrestrial adversary, which is able to launch the attacks by distributing the false tile grids to the AUEs. To address this vulnerability and enhance the location privacy of AUEs, we then propose a Colonel Blotto (CB) game framework to formulate the competitive tile grid allocation problem for the HAP and the adversary. The attack-defense interaction between the adversary and the HAP as a defender in their tile grid allocations to the AUEs is formulated as a CB game, which models the competition of two players for limited resources over multiple battlefields. Moreover, we derive the mixed-strategy Nash equilibria of the game for both symmetric and asymmetric tile grids between two players. Simulation results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

Accurately constructing dynamic network topology is one of the core tasks to provide on-demand security services to the ubiquitous network. Existing schemes cannot accurately construct dynamic network topologies in time. In this paper, we propose a novel scheme to construct the ubiquitous network topology. Firstly, ubiquitous network nodes are divided into three categories: terminal node, sink node, and control node. On this basis, we propose two operation primitives (i.e., addition and subtraction) and three atomic operations (i.e., intersection, union, and fusion), and design a series of algorithms to describe the network change and construct the network topology. We further use our scheme to depict the specific time-varying network topologies, including Satellite Internet and Internet of things. It demonstrates that their communication and security protection modes can be efficiently and accurately constructed on our scheme. The simulation and theoretical analysis also prove that the efficiency of our scheme, and effectively support the orchestration of protection capabilities.

With the help of satellites, Internet of Things (IoT) applications such as remote monitoring and ocean exploration can be realized. However, the network is vulnerable to malicious attacks due to the limited resources of satellite IoT (S-IoT) terminals and the openness of communication links. Ensuring that legitimate users can only access sensitive data remains a major concern. In this paper, a zero-trust access management model integrated with satellites, network and identity infrastructure, as well as the authentication protocol for S-IoT terminal security, was designed. The protocol adopts the Chinese cryptographic algorithms SM2, SM3, and SM4, combined with the Physical Unclonable Function (PUF) to achieve key agreement and bidirectional authentication. The security of this protocol is further analyzed, and its security, function, and performance are compared with other related protocols. Experimental results show that compared with existing similar schemes, the proposed protocol can more effectively consider the security requirements of S-IoT and reduce communication costs.

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.

The Space-Air-Ground Integrated Networks (SAGINs) converges the rapidly developing ground, aerial and satellite communication networks to provide users with efficient and personalized services through the multi-layer network architecture. The complex structure, inconsistent communication protocol, and incompatible equipment significantly affect the information transmission efficiency in SAGINs. The application of Software Defined Network (SDN) technology promotes global deployment of the SAGIN resources. Aiming at the information transmission bottleneck between controller and switch nodes in the software-defined SAGINs, a dynamic handover transmission control scheme based on the queuing game model is proposed. Considering long transmission delay links in the SAGINs, the traditional queuing game model is improved to reduce the number of information interactions. The users’ service value are defined as the random distribution to describe the various requirements in the network. In addition, the relationship between the social welfare and the arrival rate of the transmission control system is discussed under two modes of observable and unobservable controller queues. Through theoretical analysis and calculation, the unique handover arrival rate is obtained to make the social welfare equal in the two modes. When the arrival rate is less than the handover threshold, the controller queue is set to unobservable mode, and otherwise to observable mode. Then, the system can operate in an optimal way to obtain the maximum benefit. At last, numerical and system simulation verify the effectiveness of the scheme.

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.