Energy-theft detection issues for advanced metering infrastructure in smart grid

Abstract

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.

Privacy Violations in Constrained Micro-Grids: Adversarial Cases

“…We postulate that privacy leaks can be maliciously exploited for energy theft or discourage use of the network by internal users. By inference about household appliance usage patterns as a subgoal towards the goal of undetected energy data modification (energy theft), we extend the attack tree for energy theft proposed by Jiang et al [24].…”

“…Dependability encompasses reliability, safety and maintainability [27] and security goals include availability, integrity, confidentiality and privacy [24] [28] [29].…”

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

“…In particular, the widespread deployment of smart meters, renewable generation, smart load controls, energy storage, and plug-in hybrid vehicles will require fundamental changes in the operational concepts and principal components of the grid, in order to achieve real-time operation and control. Fraud detection and particularly detection of energy theft is one of most important concerns in the smart grid [18,22]. Data analytic methods can play an important role in identifying abnormal consumption trends and possible malicious activities in such systems.…”

“…Daisuke et al [22] used ARMA and LOF methods in an adversarial environment to detect attacks in data collected using advanced meter infrastructure (AMI). Rong et al [18] compared classification-based, state-based, and game theory-based methods in energy-theft detection schemas.…”

A brief analysis of recent developments in smart grid

“…This technique use the data collected from various sources to confirm an on-going theft attempt. It is capable of detecting different types of energy theft precisely by using sensors [43,44].…”