{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110655"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110655","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"AI-driven methods for resiliency and security assessment: the case for autonomous driving system and HPC storage system","abstract":"Nowadays, computing systems are used extensively in mission-critical exploration, transportation, scientific study, and manufacturing. With the advances in computation technologies, computing systems have become ever more complex. Due to the system’s complexity, it is increasingly hard for humans operators to monitor, assess, and manage the system directly. Moreover, traditional model-based or rule-based assessment techniques cannot provide sufficient coverages because of the wide range of use cases and failure modes of complex systems. Recently artificial intelligence (AI)-driven methods are used for timely accurate and high-coverage assessments in complex systems because of their ability to learn from data without explicitly modeling the complex systems. This thesis discusses our work on AI-driven assessment methods—RoboTack, DiverseAV, and Kaleidoscope—in the domain of security (RoboTack) and reliability (DiverseAV, Kaleidoscope) assessment in two critical use cases: autonomous driving systems (ADS) and high-performance computing (HPC) storage systems. We show that by using artificial intelligence and machine learning-based techniques, we can perform high-accuracy, high-coverage security, or reliability assessments of large-scale, complex systems efficiently in real-time.","abstract_html":"Nowadays, computing systems are used extensively in mission-critical exploration, transportation, scientific study, and manufacturing. With the advances in computation technologies, computing systems have become ever more complex. Due to the system’s complexity, it is increasingly hard for humans operators to monitor, assess, and manage the system directly. Moreover, traditional model-based or rule-based assessment techniques cannot provide sufficient coverages because of the wide range of use cases and failure modes of complex systems. Recently artificial intelligence (AI)-driven methods are used for timely accurate and high-coverage assessments in complex systems because of their ability to learn from data without explicitly modeling the complex systems. This thesis discusses our work on AI-driven assessment methods—RoboTack, DiverseAV, and Kaleidoscope—in the domain of security (RoboTack) and reliability (DiverseAV, Kaleidoscope) assessment in two critical use cases: autonomous driving systems (ADS) and high-performance computing (HPC) storage systems. We show that by using artificial intelligence and machine learning-based techniques, we can perform high-accuracy, high-coverage security, or reliability assessments of large-scale, complex systems efficiently in real-time.","abstract_has_math":false,"creators":["Cui, Shengkun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kalbarczyk, Zbigniew T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:23Z","date_published":"2021-09-17T02:34:23Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Autonomous Driving System","Autonomous Vehicle","HPC System","Artificial Intelligence","Machine Learning","Fault-tolerant System","Reliability","Security","Adversarial Attack","Fault Detection","Failure Localization","Failure Diagnosis"],"languages":["en"],"rights":["Copyright 2021 Shengkun Cui"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110655","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kalbarczyk, Zbigniew T."]},{"key":"dc:creator","label":"Author","values":["Cui, Shengkun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:23Z","2023-09-17T02:34:57Z","2021-04-14","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Autonomous Driving System","Autonomous Vehicle","HPC System","Artificial Intelligence","Machine Learning","Fault-tolerant System","Reliability","Security","Adversarial Attack","Fault Detection","Failure Localization","Failure Diagnosis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Shengkun Cui"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110655"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Nowadays, computing systems are used extensively in mission-critical exploration, transportation, scientific study, and manufacturing. With the advances in computation technologies, computing systems have become ever more complex. Due to the system’s complexity, it is increasingly hard for humans operators to monitor, assess, and manage the system directly. Moreover, traditional model-based or rule-based assessment techniques cannot provide sufficient coverages because of the wide range of use cases and failure modes of complex systems. Recently artificial intelligence (AI)-driven methods are used for timely accurate and high-coverage assessments in complex systems because of their ability to learn from data without explicitly modeling the complex systems. This thesis discusses our work on AI-driven assessment methods—RoboTack, DiverseAV, and Kaleidoscope—in the domain of security (RoboTack) and reliability (DiverseAV, Kaleidoscope) assessment in two critical use cases: autonomous driving systems (ADS) and high-performance computing (HPC) storage systems. We show that by using artificial intelligence and machine learning-based techniques, we can perform high-accuracy, high-coverage security, or reliability assessments of large-scale, complex systems efficiently in real-time.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Shengkun Cui, accepted the attached license on 2021-04-14 at 11:34.","The student, Shengkun Cui, submitted this Thesis for approval on 2021-04-14 at 11:39.","This Thesis was approved for publication on 2021-04-14 at 14:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16284 on 2021-09-16 at 17:02:53","Made available in DSpace on 2021-09-17T02:34:23Z (GMT). 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With the advances in computation technologies, computing systems have become ever more complex. Due to the system’s complexity, it is increasingly hard for humans operators to monitor, assess, and manage the system directly. Moreover, traditional model-based or rule-based assessment techniques cannot provide sufficient coverages because of the wide range of use cases and failure modes of complex systems. Recently artificial intelligence (AI)-driven methods are used for timely accurate and high-coverage assessments in complex systems because of their ability to learn from data without explicitly modeling the complex systems. This thesis discusses our work on AI-driven assessment methods—RoboTack, DiverseAV, and Kaleidoscope—in the domain of security (RoboTack) and reliability (DiverseAV, Kaleidoscope) assessment in two critical use cases: autonomous driving systems (ADS) and high-performance computing (HPC) storage systems. We show that by using artificial intelligence and machine learning-based techniques, we can perform high-accuracy, high-coverage security, or reliability assessments of large-scale, complex systems efficiently in real-time.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Shengkun Cui, accepted the attached license on 2021-04-14 at 11:34.","The student, Shengkun Cui, submitted this Thesis for approval on 2021-04-14 at 11:39.","This Thesis was approved for publication on 2021-04-14 at 14:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16284 on 2021-09-16 at 17:02:53","Made available in DSpace on 2021-09-17T02:34:23Z (GMT). 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