中文摘要：

With the proliferation of smart grid research,the Advanced Metering Infrastructure(AMI) has become the first ubiquitous and fixed computing platform. However,due to the unique characteristics of AMI,such as complex network structure,resource-constrained smart meter,and privacy-sensitive data,it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than $25 billion every year due to energy theft around the world. To address this challenge,in this paper,we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically,an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then,we summarize the current AMI energy-theft detection schemes into three categories,i.e.,classification-based,state estimation-based,and game theory-based ones,and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions,we also explore some open challenges and potential solutions for energy-theft detection.

英文摘要：

With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.