In recent years, the smart grid has been recognized as an important form of the Internet of Things application. In the smart grid, as an energy-based cyber-physical system, the advanced metering infrastructure (AMI) will be developed to monitor and control the power grid by integrating computing and networking components to ensure stable and efficient operation. The AMI is vulnerable to cyber attacks, especially data integrity attacks. There have been a number of research efforts on detecting such attacks. Nonetheless, most of existing schemes either rely on predefined thresholds or require external knowledge. This may lead to low detection accuracy when the thresholds are improperly defined, and where there is a lack of the external knowledge. To address these issues, in this paper, we propose a Gaussian-mixture model-based detection scheme to mitigate data integrity attacks. Not relying upon the predefined thresholds or external knowledge, our developed scheme operates through narrowing the range of normal data, which can be obtained through clustering the historical data and learning minimum and maximum values or distance values to each center of individual clusters. To evaluate the effectiveness of our proposed scheme, we conduct performance simulation based on the ElectricityLoadDiagrams20112014 data set, and then analyze the effectiveness of the proposed scheme with respect to detection accuracy and overhead. The results of our investigation show that our scheme could achieve a higher detection rate, and a lower error rate, in comparison to existing schemes based on the Min-Max model.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.

In the smart grid, the Advanced Metering Infrastructure (AMI) will be deployed to monitor and control the power grid by integrating both computing and networking components to achieve stable and efficient operation. The AMI is vulnerable to cyber attacks, especially in the form of data integrity attacks. A number of research efforts have been devoted to detecting such attacks. Nonetheless, the majority of existing schemes either rely on a pre-defined threshold, or require external knowledge. This leaves open the possibility for low detection accuracy when the threshold is improperly defined, and where there is a lack of the requisite external knowledge. To address this issue, in this paper we propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to combat data integrity attacks. Not relying upon the pre-defined threshold or external knowledge, our scheme operates by narrowing the range of normal data that can be obtained by clustering the historical data and learning the minimum and maximum values of individual clusters. To validate the effectiveness of our scheme, we conduct performance evaluation based on the ElectricityLoadDiagrams20112014 data set, and analyze the effectiveness of the proposed scheme with respect to detection accuracy.The results of our investigation demonstrate that our scheme can achieve a higher detection rate, and lower error rate, in comparison with existing schemes based on the Min-Max model.

In this paper, we address the issue of designing an effective distributed real-time pricing scheme in the smart grid and investigating its security resilience when the data integrity attack is in place. Different from existing research efforts, in this paper we develop a distributed real-time pricing scheme, which can maximize the welfare of all participants and improve the resilience to system failures, as well as consider both renewable and traditional power resources. By leveraging the distributed approach, we leverage the gradient projection mechanism to solve the distributed real-time pricing problem in participants' smart meters to improve the resilience to system failures. We also investigate the vulnerabilities of the distributed real-time pricing scheme by considering one typical data integrity attack, which can inject false data into communication interfaces. Via a combination of both theoretical analysis and performance evaluation, we demonstrate that the proposed distributed scheme can effectively guide the participants to achieve individual welfare maximization. Our findings also show that data integrity attacks can disrupt the distributed real-time pricing, posing a damage to the welfare of participants.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources, and can effectively guide the participants to achieve individual welfare maximization. Particularly, we develop a Lagrangian- based approach that transforms the global optimization conducted by the power company to distributed optimization problems. We show that these distributed problems are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks, i.e., injecting false data into demand-users and injecting false data into supply- users. Through a combination of theoretical analysis and performance evaluation, our data shows that the proposed real-time pricing scheme can effectively guide the participants to achieve welfare maximization. Our data also shows that data integrity attacks can effectively disrupt the results of real-time pricing decisions, posing welfare reduction on participants.

To enhance the reliability and efficiency of energy service in the smart grid, the concept of the microgrid has been proposed. Nonetheless, how to secure the dynamic microgrid partition process is essential in the smart grid. In this paper, we address the security issue of the dynamic microgrid partition process and systematically investigate three false data injection attacks against the dynamic microgrid partition process. Particularly, we first discussed the dynamic microgrid partition problem based on a Connected Graph Constrained Knapsack Problem (CGKP) algorithm. We then developed a theoretical model and carried out simulations to investigate the impacts of these false data injection attacks on the effectiveness of the dynamic microgrid partition process. Our theoretical and simulation results show that the investigated false data injection attacks can disrupt the dynamic microgrid partition process and pose negative impacts on the balance of energy demand and supply within microgrids such as an increased number of lack-nodes and increased energy loss in microgrids.