{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2028"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2028","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Cyber attacks detection in electric vehicles fast charging stations using wavelets and deep learning","abstract":"The Integration of the electric vehicles fast charging stations with smart electric grid allows the electric vehicles to provide regulation services (e.g., voltage and frequency) to the grid through the Vehicle-to-Grid (V2G) concept. An effective service provision dictates the integration of communication networks to smart grid components, which makes many of the smart grid assets prone to cyber vulnerability threats. This thesis addresses the impact of cyber-attack in the electric vehicles fast charging stations and its consequences on the power quality. The thesis proposes a cyber-attacks detection approach based on signal processing and deep learning to early detect such attacks. The proposed detection approach has the ability to learn, detect and classify such attacks under different operating conditions and using different time resolutions of smart meters. The results have shown that the proposed approach was effective in detecting the cyber-attacks at an average accuracy of nearly 99.4%.","abstract_html":"The Integration of the electric vehicles fast charging stations with smart electric grid allows the electric vehicles to provide regulation services (e.g., voltage and frequency) to the grid through the Vehicle-to-Grid (V2G) concept. An effective service provision dictates the integration of communication networks to smart grid components, which makes many of the smart grid assets prone to cyber vulnerability threats. This thesis addresses the impact of cyber-attack in the electric vehicles fast charging stations and its consequences on the power quality. The thesis proposes a cyber-attacks detection approach based on signal processing and deep learning to early detect such attacks. The proposed detection approach has the ability to learn, detect and classify such attacks under different operating conditions and using different time resolutions of smart meters. The results have shown that the proposed approach was effective in detecting the cyber-attacks at an average accuracy of nearly 99.4%.","abstract_has_math":false,"creators":["Abu Nassar, Ahmad"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Ibrahim, Walid Morsi"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-01","date_published":"2022-04-01","updated_at":"2026-07-24T05:35:43Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2028","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ibrahim, Walid Morsi"]},{"key":"dc:creator","label":"Author","values":["Abu Nassar, Ahmad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-17T17:57:08Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-17T17:57:08Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2028"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Integration of the electric vehicles fast charging stations with smart electric grid allows the electric vehicles to provide regulation services (e.g., voltage and frequency) to the grid through the Vehicle-to-Grid (V2G) concept. An effective service provision dictates the integration of communication networks to smart grid components, which makes many of the smart grid assets prone to cyber vulnerability threats. This thesis addresses the impact of cyber-attack in the electric vehicles fast charging stations and its consequences on the power quality. The thesis proposes a cyber-attacks detection approach based on signal processing and deep learning to early detect such attacks. The proposed detection approach has the ability to learn, detect and classify such attacks under different operating conditions and using different time resolutions of smart meters. The results have shown that the proposed approach was effective in detecting the cyber-attacks at an average accuracy of nearly 99.4%."]},{"key":"dc:title","label":"Title","values":["Cyber attacks detection in electric vehicles fast charging stations using wavelets and deep learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ibrahim, Walid Morsi"],"dc:creator":["Abu Nassar, Ahmad"],"dc:date.accessioned":["2025-10-17T17:57:08Z"],"dc:date.available":["2025-10-17T17:57:08Z"],"dc:date.issued":["2022-04-01"],"dc:description.abstract":["The Integration of the electric vehicles fast charging stations with smart electric grid allows the electric vehicles to provide regulation services (e.g., voltage and frequency) to the grid through the Vehicle-to-Grid (V2G) concept. An effective service provision dictates the integration of communication networks to smart grid components, which makes many of the smart grid assets prone to cyber vulnerability threats. This thesis addresses the impact of cyber-attack in the electric vehicles fast charging stations and its consequences on the power quality. The thesis proposes a cyber-attacks detection approach based on signal processing and deep learning to early detect such attacks. The proposed detection approach has the ability to learn, detect and classify such attacks under different operating conditions and using different time resolutions of smart meters. The results have shown that the proposed approach was effective in detecting the cyber-attacks at an average accuracy of nearly 99.4%."],"dc:identifier.uri":["https://hdl.handle.net/10155/2028"],"dc:language.iso":["en"],"dc:title":["Cyber attacks detection in electric vehicles fast charging stations using wavelets and deep learning"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:43Z"}