A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures

By Stephen E. McLaughlin, Brett Holbert, Ahmed M. Fawaz, Robin Berthier, Saman A. Zonouz. 2013

242 citations recorded by the source corpus.

The advanced metering infrastructure (AMI) is a crucial component of the smart grid, replacing traditional analog devices with computerized smart meters. Smart meters have not only allowed for efficient management of many end-users, but also have made AMI an attractive target for remote exploits and local physical tampering with the end goal of stealing energy. While smart meters posses multiple sensors and data sources that can indicate energy theft, in practice, the individual methods exhibit many false positives. In this paper, we present AMIDS, an AMI intrusion detection system that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft. AMIDS combines meter audit logs of physical and cyber events with consumption data to more accurately model and detect theft-related behavior. Our experimental results on normal and anomalous load profiles show that AMIDS can identify energy theft efforts with high accuracy. Furthermore, AMIDS correctly identified legitimate load profile changes that more elementary analyses classified as malicious.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

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Citations in the corpus

A survey of data fusion in smart city applications (2019, neutral): “For instance, work in [214] uses the clustering algorithm to identify energy theft accurately while reducing potential false positives.”
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids (2018, background): “ior usually includes bypassing the electricity meter, tampering the meter reading, or hacking the meter [2].”
Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, methodology): “proposed an AMI Intrusion Detection System (AMIDS) that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft[38, 39].”
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid (2016, background): “Apart from this, tampering with the electric meters and billing alterations by the legitimate employees are other causes behind electric theft [2], [3].”
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach (2019, background): “The main causes of NTL are billing delay and irregularities, energy theft, faulty energy meters, fraud, and unpaid bills [9].”
Electricity Theft Detection in AMI Using Customers’ Consumption Patterns (2016, methodology): “AMIDS collects evidences of malicious behavior from three types of information sources: 1) cyber-side network and host-based IDSs; 2) on-meter anti-tampering sensors; and 3) power measurement-based anomalous consumption detectors through nonintrusive load monitoring (NILM).”
A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, methodology): “This method was applied in detecting FDIA in [111], [114], [115]. the main advantage of this method is its simplicity, while the main drawback is the high sensitivity to the noise in the samples.”