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Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation
Ismail, Muhammad Shaaban, Mostafa Naidu, Mahesh Serpedin, Erchin
Ismail, Muhammad
Shaaban, Mostafa
Naidu, Mahesh
Serpedin, Erchin
Date
2020-02
Advisor
Type
Article
Peer-Reviewed
Postprint
Peer-Reviewed
Postprint
Degree
Description
Abstract
Unlike the existing research that focuses on detecting electricity theft cyber-attacks in the consumption domain, this paper investigates electricity thefts at the distributed generation (DG) domain. In this attack, malicious customers hack into the smart meters monitoring their renewable-based DG units and manipulate their readings to claim higher supplied energy to the grid and hence falsely overcharge the utility company. Deep machine learning is investigated to detect such a malicious behavior. We aim to answer three main questions in this paper: a) What are the cyber-attack functions that can be applied by malicious customers to the generation data in order to falsely overcharge the utility company? b) What sources of data can be used in order to detect these cyber-attacks by the utility company? c) Which deep machine learning-model should be used in order to detect these cyber-attacks? Our investigation revealed that integrating various data from the DG smart meters, meteorological reports, and SCADA metering points in the training of a deep convolutional-recurrent neural network offers the highest detection rate (99:3%) and lowest false alarm (0:22%).