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Security of Smart Cyber-Physical Grids: A Deep Learning Approach

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Title: Security of Smart Cyber-Physical Grids: A Deep Learning Approach
Author: Sakhnini, Jacob
Department: School of Engineering
Program: Engineering
Advisor: Karimipour, Hadis
Abstract: Cyber physical systems are widely used in critical infrastructure; among the most notable applications is the smart cyber-physical grid. The smart grid technologies are accompanied with various advantages including more efficient power generation and increased integration of green energy sources. As such, many cities around the world are investing in smart cyber-physical grid technologies. The use of this technology, however, comes with great risk to cyber threats. Furthermore, current state of the art defense methods lack in robustness, scalability,and computational efficiency. This thesis presents a deep learning based solution for attack detection in cyber-physical systems, particularly in the case of the smart cyber-physical grid.The research methods implemented in this thesis focus on improving robustness, scalability,and computational efficiency of intelligent attack detection algorithms by presenting heuristic methods for feature extraction and a novel deep learning approach that proved robust to varying attack sparsity and data imbalance.
Date: 2020-04
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