Work

A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

Year: 2019

Type: article

Abstract: Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%

Source: IEEE Access

DOI: 10.1109/access.2019.2920326

Authors: Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi, Kim-Kwang Raymond Choo, Henry Leung

Institutions: University of Guelph, Kennesaw State University, The University of Texas at San Antonio, University of Calgary

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,750