{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101373"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101373","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Application of deep learning for predicting schedules in real-time systems","abstract":"Hard real-time systems are often used in safety critical systems: a task missing a deadline can be catastrophic for the system and endanger human lives. To guarantee that it meets every deadline, hard real-time systems are designed to have deterministic behavior. However, such determinism is prone to timing inference attacks. Using an analytical approach, an inference attack can be launched with a priori knowledge about the task-set. However, the advancements in deep learning opens new methods that can be used to carry out such attacks. We believe that the current state of machine learning algorithms is powerful enough to launch the attack without the complete a priori knowledge. Therefore, we propose a novel architecture that will accurately predict future occurrences of target tasks in systems using real-time scheduling algorithms. We intend to use minimal information, for instance by observing only the sequences of busy intervals and rest intervals. The architecture will: infer size of the task-set, map tasks to each time steps of busy intervals and predict future task execution.","abstract_html":"Hard real-time systems are often used in safety critical systems: a task missing a deadline can be catastrophic for the system and endanger human lives. To guarantee that it meets every deadline, hard real-time systems are designed to have deterministic behavior. However, such determinism is prone to timing inference attacks. Using an analytical approach, an inference attack can be launched with a priori knowledge about the task-set. However, the advancements in deep learning opens new methods that can be used to carry out such attacks. We believe that the current state of machine learning algorithms is powerful enough to launch the attack without the complete a priori knowledge. Therefore, we propose a novel architecture that will accurately predict future occurrences of target tasks in systems using real-time scheduling algorithms. We intend to use minimal information, for instance by observing only the sequences of busy intervals and rest intervals. The architecture will: infer size of the task-set, map tasks to each time steps of busy intervals and predict future task execution.","abstract_has_math":false,"creators":["Kim, Kyo Hyun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Mohan, Sibin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:47:30Z","date_published":"2018-09-04T20:47:30Z","updated_at":"2026-07-22T22:24:40Z","subjects":["machine learning","real-time systems","task prediction","real-time scheduling"],"languages":["en"],"rights":["Copyright 2018 Kyo Hyun Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101373","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mohan, Sibin"]},{"key":"dc:creator","label":"Author","values":["Kim, Kyo Hyun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:47:30Z","2018-04-25","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","real-time systems","task prediction","real-time scheduling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Kyo Hyun Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101373"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Hard real-time systems are often used in safety critical systems: a task missing a deadline can be catastrophic for the system and endanger human lives. To guarantee that it meets every deadline, hard real-time systems are designed to have deterministic behavior. However, such determinism is prone to timing inference attacks. Using an analytical approach, an inference attack can be launched with a priori knowledge about the task-set. However, the advancements in deep learning opens new methods that can be used to carry out such attacks. We believe that the current state of machine learning algorithms is powerful enough to launch the attack without the complete a priori knowledge. Therefore, we propose a novel architecture that will accurately predict future occurrences of target tasks in systems using real-time scheduling algorithms. We intend to use minimal information, for instance by observing only the sequences of busy intervals and rest intervals. The architecture will: infer size of the task-set, map tasks to each time steps of busy intervals and predict future task execution.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Kyo Hyun Kim, accepted the attached license on 2018-04-24 at 18:16.","The student, Kyo Hyun Kim, submitted this Thesis for approval on 2018-04-24 at 18:25.","This Thesis was approved for publication on 2018-04-25 at 15:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12463 on 2018-08-31 at 17:30:23","Made available in DSpace on 2018-09-04T20:47:30Z (GMT). No. of bitstreams: 2 KIM-THESIS-2018.pdf: 334226 bytes, checksum: 312c2bf84a75cb5270f3b7b111e157d8 (MD5) LICENSE.txt: 4209 bytes, checksum: f54ef272252e93942e5017735a636434 (MD5) Previous issue date: 2018-04-25","Embargo set by: Seth Robbins for item 107458 Lift date: 2020-09-04T20:47:38Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107458 Lift date: 2020-09-04T20:50:11Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Open Restriction set for Item 107458 on 2019-07-30T19:35:40Z with date null by mhoh2@illinois.edu.","Open Restriction set for Item 107458 on 2019-07-30T19:35:44Z with date null by mhoh2@illinois.edu.","Open"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Application of deep learning for predicting schedules in real-time systems"]}]}],"canonical_facts":{"dc:contributor":["Mohan, Sibin"],"dc:creator":["Kim, Kyo Hyun"],"dc:date":["2018-09-04T20:47:30Z","2018-04-25","2018-05"],"dc:description":["Hard real-time systems are often used in safety critical systems: a task missing a deadline can be catastrophic for the system and endanger human lives. To guarantee that it meets every deadline, hard real-time systems are designed to have deterministic behavior. However, such determinism is prone to timing inference attacks. Using an analytical approach, an inference attack can be launched with a priori knowledge about the task-set. However, the advancements in deep learning opens new methods that can be used to carry out such attacks. We believe that the current state of machine learning algorithms is powerful enough to launch the attack without the complete a priori knowledge. Therefore, we propose a novel architecture that will accurately predict future occurrences of target tasks in systems using real-time scheduling algorithms. We intend to use minimal information, for instance by observing only the sequences of busy intervals and rest intervals. The architecture will: infer size of the task-set, map tasks to each time steps of busy intervals and predict future task execution.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Kyo Hyun Kim, accepted the attached license on 2018-04-24 at 18:16.","The student, Kyo Hyun Kim, submitted this Thesis for approval on 2018-04-24 at 18:25.","This Thesis was approved for publication on 2018-04-25 at 15:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12463 on 2018-08-31 at 17:30:23","Made available in DSpace on 2018-09-04T20:47:30Z (GMT). No. of bitstreams: 2 KIM-THESIS-2018.pdf: 334226 bytes, checksum: 312c2bf84a75cb5270f3b7b111e157d8 (MD5) LICENSE.txt: 4209 bytes, checksum: f54ef272252e93942e5017735a636434 (MD5) Previous issue date: 2018-04-25","Embargo set by: Seth Robbins for item 107458 Lift date: 2020-09-04T20:47:38Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107458 Lift date: 2020-09-04T20:50:11Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Open Restriction set for Item 107458 on 2019-07-30T19:35:40Z with date null by mhoh2@illinois.edu.","Open Restriction set for Item 107458 on 2019-07-30T19:35:44Z with date null by mhoh2@illinois.edu.","Open"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/101373"],"dc:language":["en"],"dc:rights":["Copyright 2018 Kyo Hyun Kim"],"dc:subject":["machine learning","real-time systems","task prediction","real-time scheduling"],"dc:title":["Application of deep learning for predicting schedules in real-time systems"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:40Z"}