In smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. Reporting false readings not only causes hefty financial losses to the utility but may also degrade the grid performance because the reported readings are used for energy management. This paper is the first work that investigates this problem in the net-metering system, in which one SM is used to report the difference between the power consumed and the power generated. First, we prepare a benign dataset for the net-metering system by processing a real power consumption and generation dataset. Then, we propose a new set of attacks tailored for the net-metering system to create malicious dataset. After that, we analyze the data and we found time correlations between the net meter readings and correlations between the readings and relevant data obtained from trustworthy sources such as the solar irradiance and temperature. Based on the data analysis, we propose a general multi-data-source deep hybrid learning-based detector to identify the false-reading attacks. Our detector is trained on net meter readings of all customers besides data from the trustworthy sources to enhance the detector performance by learning the correlations between them. The rationale here is that although an attacker can report false readings, he cannot manipulate the solar irradiance and temperature values because they are beyond his control. Extensive experiments have been conducted, and the results indicate that our detector can identify the false-reading attacks with high detection rate and low false alarm.
Smart Grids are evolving as the next generation power systems that involve changes in the traditional ways of generation, transmission and distribution of power. Advanced Metering Infrastructure (AMI) is one of the key components in smart grids. An AMI comprises of systems and networks, that collects and analyzes data received from smart meters. In addition, AMI also provides intelligent management of various power-related applications and services based on the data collected from smart meters. Thus, AMI plays a significant role in the smooth functioning of smart grids. AMI is a privileged tar…
The Advanced Metering Infrastructure (AMI) is a vital element in the current development of the smart grid. AMI technologies provide electric utilities with an effective way of continuous monitoring and remote control of smart grid components. However, owing to its increasing scale and cyber-physical nature, the AMI has been faced with security threats in both cyber and physical domains. This paper provides a comprehensive review of the crucial cyber-physical attacks and counter defense mechanisms in the AMI. First, two attack surfaces are surveyed in the AMI including the communication networ…
Cyber attacks on critical infrastructure have been an issue of importance in industry over the past several years, as well as a focus of academic research. Cyber attacks of various types and magnitude have been on the rise targeted specifically at power grids. A successful attack on the power grid could result in significant impacts including grid shutdown, cascading failures, damage to the infrastructure, and potential harm to people. Power grid infrastructure are critical in nature, they enable operations for residential, commercial, industrial, and government users across critical infrastru…
Advanced Metering Infrastructure (AMI) have rapidly become a topic of international interest as governments have sponsored their deployment for the purposes of utility service reliability and efficiency, e.g., water and electricity conservation. Two problems plague such deployments. First is the protection of consumer privacy. Second is the problem of huge amounts of data from such deployments. A new architecture is proposed to address these problems through the use of Aggregators, which incorporate temporary data buffering and the modularization of utility grid analysis. These Aggregators are…
In Advanced Metering Infrastructure (AMI) networks, smart meters should send fine-grained power consumption readings to electric utilities to perform real-time monitoring and energy management. However, these readings can leak sensitive information about consumers' activities. Various privacy-preserving schemes for collecting fine-grained readings have been proposed for AMI networks. These schemes aggregate individual readings and send an aggregated reading to the utility, but they extensively use asymmetric-key cryptography which involves large computation/communication overhead. Furthermore,…
In advanced metering infrastructure (AMI), smart meters (SMs), which are installed at the consumer side, send fine-grained power consumption readings periodically to the electricity utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem that is by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission p…
In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer side to send fine-grained power consumption readings periodically to the system operator (SO) for load monitoring, energy management, billing, etc. However, fraudulent consumers launch electricity theft cyber-attacks by reporting false readings to reduce their bills illegally. These attacks do not only cause financial losses but may also degrade the grid performance because the readings are used for grid management. To identify these attackers, the existing schemes employ machine-learning models using t…
Tennessee Tech. University（田纳西理工大学）；Texas A&M University at Qatar（德州农工大学卡塔尔分校）
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, h…
The concept of differential privacy emerged as a strong notion to protect database privacy in an untrusted environment. Later on, researchers proposed several variants of differential privacy in order to preserve privacy in certain other scenarios, such as real-time cyber physical systems. Since then, differential privacy has rigorously been applied to certain other domains which has the need of privacy preservation. One such domain is decentralized blockchain based smart metering, in which smart meters acting as blockchain nodes sent their real-time data to grid utility databases for real-tim…
In Advanced Metering Infrastructure (AMI) systems, smart meters (SM) send fine-grained power consumption information to the utility company, yet this power consumption information can uncover sensitive information about the consumers' lifestyle. To allow the utility company to gather the power consumption information while safeguarding the consumers' privacy, different methods that broadly utilize symmetric key and asymmetric key cryptography operation have been generally utilized. In this paper, we propose an effective method that uses symmetric key cryptography and hashing operation to gathe…
The University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；University of Illinois Springfield（伊利诺伊大学斯普林菲尔德分校）
Effective detection of energy theft can prevent revenue losses of utility companies and is also important for smart grid security. In recent years, enabled by the massive fine-grained smart meter data, deep learning (DL) approaches are becoming popular in the literature to detect energy theft in the advanced metering infrastructure (AMI). However, as neural networks are shown to be vulnerable to adversarial examples, the security of the DL models is of concern. In this work, we study the vulnerabilities of DL-based energy theft detection through adversarial attacks, including single-step attac…
Smart cities deploy large numbers of sensors and collect a tremendous amount of data from them. For example, Advanced Metering Infrastructures (AMIs), which consist of physical meters that collect usage data about public utilities such as power and water, are an important building block in a smart city. In a typical sensor network, the measurement devices are connected through a computer network, which exposes them to cyber attacks. Furthermore, the data is centrally managed at the operator's servers, making it vulnerable to insider threats. Our goal is to protect the integrity of data collect…
