The integration of social networking concepts with Internet of Vehicles (IoV) has led to the novel paradigm “Social Internet of Vehicles (SIoV),” which enables vehicles to establish social relationships autonomously to improve traffic conditions and service discovery. There is a growing requirement for effective trust management in the SIoV, considering the critical consequences of acting on misleading information spread by malicious nodes. However, most existing trust models are rater-based, where the reputation information of each node is stored in other nodes it has interacted with. This is not suitable for vehicular environment due to the ephemeral nature of the network. To fill this gap, we propose a Ratee-based Trust Management (RTM) system, where each node stores its own reputation information rated by others during past transactions, and a credible CA server is introduced to ensure the integrality and the undeniability of the trust information. RTM is built based on the concept of SIoV, so that the relationships established between nodes can be used to increase the accuracy of the trustworthiness. Experimental results demonstrate that our scheme achieves faster information propagation and higher transaction success rate than the rater-based method, and the time cost when calculating trustworthiness can meet the demand of vehicular networks.

False data injection (FDI) attack is a hot topic in cyber-physical systems (CPSs). Attackers inject bad data into sensors or return false data to the controller to cause the inaccurate state estimation. Although there exists many detection approaches, such as bad data detector (BDD), sequence pattern mining, and machine learning methods, a smart attacker still can inject perfectly false data to go undetected. In this paper, we focus on the advanced false data injection (AFDI) attack and its detection method. An AFDI attack refers to the attack where a malicious entity accurately and successively changes sensory data, making the normal system state continuously evaluated as other legal system states, causing wrong outflow of controllers. The attack can lead to an automatic and long-term system failure/performance degradation. We first depict the AFDI attack model and analyze limitations of existing detectors for detecting AFDI. Second, we develop an approach based on machine learning, which utilizes the k-Nearest Neighbor (KNN) technique and heterogeneous data including sensory data and system commands to implement a classifier for detecting AFDI attacks. Finally, simulation experiments are given to demonstrate AFDI attack impact and the effectiveness of the proposed method for detecting AFDI attacks.

In a global positioning system (GPS) passive radar, a high resolution requires a high sampling frequency, which increases the computational load. Balancing the computational load and the range resolution is challenging. This paper presents a method to trade off the range resolution and the computational load by experimentally determining the optimal sampling frequency through an analysis of multiple sets of GPS satellite data at different sampling frequencies. The test data are used to construct a range resolution-sampling frequency trade-off model using least-squares estimation. The theoretical analysis shows that the experimental data are the best fit using smoothing and nth-order derivative splines. Using field GPS C/A code signal-based GPS radar, the trade-off between the optimal sampling frequency is determined to be in the 20 461.25 -24 553.5 kHz range, which supports a resolution of 43–48 m. Compared with the conventional method, the CPU time is reduced by approxi-mately 50%.

Many previous works only focused on the cascading failure of global coupling of one-to-one structures in interdependent networks, but the local coupling of dual coupling structures has rarely been studied due to its complex structure. This will result in a serious consequence that many conclusions of the one-to-one structure may be incorrect in the dual coupling network and do not apply to the smart grid. Therefore, it is very necessary to subdivide the dual coupling link into a top-down coupling link and a bottom-up coupling link in order to study their influence on network robustness by combining with different coupling modes. Additionally, the power flow of the power grid can cause the load of a failed node to be allocated to its neighboring nodes and trigger a new round of load distribution when the load of these nodes exceeds their capacity. This means that the robustness of smart grids may be affected by four factors, i.e., load redistribution, local coupling, dual coupling link and coupling mode; however, the research on the influence of those factors on the network robustness is missing. In this paper, firstly, we construct the smart grid as a two-layer network with a dual coupling link and divide the power grid and communication network into many subnets based on the geographical location of their nodes. Secondly, we define node importance ( $NI$ ) as an evaluation index to access the impact of nodes on the cyber or physical network and propose three types of coupling modes based on $NI$ of nodes in the cyber and physical subnets, i.e., Assortative Coupling in Subnets (ACIS), Disassortative Coupling in Subnets (DCIS), and Random Coupling in Subnets (RCIS). Thirdly, a cascading failure model is proposed for studying the effect of local coupling of dual coupling link in combination with ACIS, DCIS, and RCIS on the robustness of the smart grid against a targeted attack, and the survival rate of functional nodes is used to assess the robustness of the smart grid. Finally, we use the IEEE 118-Bus System and the Italian High-Voltage Electrical Transmission Network to verify our model and obtain the same conclusions: (I) DCIS applied to the top-down coupling link is better able to enhance the robustness of the smart grid against a targeted attack than RCIS or ACIS, (II) ACIS applied to a bottom-up coupling link is better able to enhance the robustness of the smart grid against a targeted attack than RCIS or DCIS, and (III) the robustness of the smart grid can be improved by increasing the tolerance $\alpha$ . This paper provides some guidelines for slowing down the speed of the cascading failures in the design of architecture and optimization of interdependent networks, such as a top-down link with DCIS, a bottom-up link with ACIS, and an increased tolerance $\alpha$ .