Smart meters are inherent components in advanced metering infrastructure (AMI) in the smart power grid. They are serving as the crucial interfaces through which the cyber, physical, and social domains of the smart grid can interact with each other. Due to the complicated interactions, smart meters may face a large variety of threats. In this paper, we exploit the colored Petri net to describe the information flows among units in a smart meter. Then, we propose a threat model for smart meters. Considering the constrained computation and storage resources of a smart meter, we present a collaborative intrusion detection mechanism against false data injection attack. The proposed scheme can work regardless of changes in a smart meter's software. Numerical results demonstrate the low cost and effectiveness of our proposed intrusion detection mechanism.Index Terms-Advanced metering infrastructure (AMI), collaborative intrusion detection, colored Petri net, smart grid, smart meters, spying domain, threat model.
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.
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