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

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dc.contributor.advisor Karimipour, Hadis
dc.contributor.author Sakhnini, Jacob
dc.date.accessioned 2020-04-24T16:01:44Z
dc.date.available 2020-04-24T16:01:44Z
dc.date.copyright 2020-04
dc.date.created 2020-04-07
dc.date.issued 2020-04-24
dc.identifier.uri http://hdl.handle.net/10214/17880
dc.description.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. en_US
dc.language.iso en en_US
dc.subject Artificial Intelligence en_US
dc.subject Cyber Security en_US
dc.subject Machine Learning en_US
dc.subject Attack Detection en_US
dc.title Security of Smart Cyber-Physical Grids: A Deep Learning Approach en_US
dc.type Thesis en_US
dc.degree.programme Engineering en_US
dc.degree.name Master of Applied Science en_US
dc.degree.department School of Engineering en_US
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