{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/2750"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/2750","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Securing a UAV Using Features from an EEG Signal","abstract":"This thesis focuses on an approach which entails the extraction of Beta component of the EEG (Electroencephalogram) signal of a user and uses his/her EEG beta data to generate a random AES (Advanced Encryption Standard) encryption key. This Key is used to encrypt the communication between the UAVs (Unmanned aerial vehicles) and the ground control station. UAVs have attracted both commercial and military organizations in recent years. The progress in this field has reached significant popularity, and the research has incorporated different areas from the scientific domain. UAV communication became a significant concern when an attack on a Predator UAV occurred in 2009, which allowed the hijackers to get the live video stream. Since a UAVs major function depend on its onboard auto pilot, it is important to harden the system against vulnerabilities. In this thesis, we propose a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that gets activated in case the communication of the UAV from the ground control station gets attacked. This system was validated on a commercial UAV under malicious attack conditions during which we implement a procedure where the UAV return safely to an initially deployed \"home\" position.","abstract_html":"This thesis focuses on an approach which entails the extraction of Beta component of the EEG (Electroencephalogram) signal of a user and uses his/her EEG beta data to generate a random AES (Advanced Encryption Standard) encryption key. This Key is used to encrypt the communication between the UAVs (Unmanned aerial vehicles) and the ground control station. UAVs have attracted both commercial and military organizations in recent years. The progress in this field has reached significant popularity, and the research has incorporated different areas from the scientific domain. UAV communication became a significant concern when an attack on a Predator UAV occurred in 2009, which allowed the hijackers to get the live video stream. Since a UAVs major function depend on its onboard auto pilot, it is important to harden the system against vulnerabilities. In this thesis, we propose a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that gets activated in case the communication of the UAV from the ground control station gets attacked. This system was validated on a commercial UAV under malicious attack conditions during which we implement a procedure where the UAV return safely to an initially deployed "home" position.","abstract_has_math":false,"creators":["Singandhupe, Ashutosh Ramlal"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["La, Hung M.","Feil-Seifer, David"],"committee_chairs":[],"committee_members":["Xu, Hao"],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-27T21:45:34Z","subjects":["EEG","Security","UAV"],"languages":[],"rights":["In Copyright(All Rights Reserved)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/2750","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["La, Hung M.","Feil-Seifer, David"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Xu, Hao"]},{"key":"dc:creator","label":"Author","values":["Singandhupe, Ashutosh Ramlal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-02-26T17:58:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-26T17:58:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["EEG","Security","UAV"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright(All Rights Reserved)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/2750"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis focuses on an approach which entails the extraction of Beta component of the EEG (Electroencephalogram) signal of a user and uses his/her EEG beta data to generate a random AES (Advanced Encryption Standard) encryption key. This Key is used to encrypt the communication between the UAVs (Unmanned aerial vehicles) and the ground control station. UAVs have attracted both commercial and military organizations in recent years. The progress in this field has reached significant popularity, and the research has incorporated different areas from the scientific domain. UAV communication became a significant concern when an attack on a Predator UAV occurred in 2009, which allowed the hijackers to get the live video stream. Since a UAVs major function depend on its onboard auto pilot, it is important to harden the system against vulnerabilities. In this thesis, we propose a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that gets activated in case the communication of the UAV from the ground control station gets attacked. This system was validated on a commercial UAV under malicious attack conditions during which we implement a procedure where the UAV return safely to an initially deployed \"home\" position."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Securing a UAV Using Features from an EEG Signal"]}]}],"canonical_facts":{"dc:contributor.advisor":["La, Hung M.","Feil-Seifer, David"],"dc:contributor.committeemember":["Xu, Hao"],"dc:creator":["Singandhupe, Ashutosh Ramlal"],"dc:date.accessioned":["2018-02-26T17:58:28Z"],"dc:date.available":["2018-02-26T17:58:28Z"],"dc:date.issued":["2017"],"dc:description.abstract":["This thesis focuses on an approach which entails the extraction of Beta component of the EEG (Electroencephalogram) signal of a user and uses his/her EEG beta data to generate a random AES (Advanced Encryption Standard) encryption key. This Key is used to encrypt the communication between the UAVs (Unmanned aerial vehicles) and the ground control station. UAVs have attracted both commercial and military organizations in recent years. The progress in this field has reached significant popularity, and the research has incorporated different areas from the scientific domain. UAV communication became a significant concern when an attack on a Predator UAV occurred in 2009, which allowed the hijackers to get the live video stream. Since a UAVs major function depend on its onboard auto pilot, it is important to harden the system against vulnerabilities. In this thesis, we propose a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that gets activated in case the communication of the UAV from the ground control station gets attacked. This system was validated on a commercial UAV under malicious attack conditions during which we implement a procedure where the UAV return safely to an initially deployed \"home\" position."],"dc:format":["PDF"],"dc:identifier.uri":["http://hdl.handle.net/11714/2750"],"dc:rights":["In Copyright(All Rights Reserved)"],"dc:subject":["EEG","Security","UAV"],"dc:title":["Securing a UAV Using Features from an EEG Signal"],"dc:type":["Thesis"],"thesis:degree_level":["Master's Degree"]},"updated_at":"2026-07-27T21:45:34Z"}