{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95424"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95424","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Determining cost-effective intrusion detection approaches for an advanced metering infrastructure deployment using advise","abstract":"Utilities responsible for Advanced Metering Infrastructure (AMI) networks must be able to defend themselves from a variety of potential attacks so they may achieve the goals of delivering power to consumers and maintaining the integrity of their equipment and data. Intrusion detection systems (IDSes) can play an important part in the defense of such networks. Utilities should carefully consider the strengths and weaknesses of different IDS deployment strategies to choose the most cost-effective solution. Models of adversary behavior in the presence of different IDS deployments can help with making this decision as we demonstrate through a case study that uses a model created in the ADversary VIew Security Evaluation (ADVISE) formalism (which calculates metrics used to compare different IDSes). We show how these metrics give valuable insight into the selection of the appropriate IDS architecture for an AMI network.","abstract_html":"Utilities responsible for Advanced Metering Infrastructure (AMI) networks must be able to defend themselves from a variety of potential attacks so they may achieve the goals of delivering power to consumers and maintaining the integrity of their equipment and data. Intrusion detection systems (IDSes) can play an important part in the defense of such networks. Utilities should carefully consider the strengths and weaknesses of different IDS deployment strategies to choose the most cost-effective solution. Models of adversary behavior in the presence of different IDS deployments can help with making this decision as we demonstrate through a case study that uses a model created in the ADversary VIew Security Evaluation (ADVISE) formalism (which calculates metrics used to compare different IDSes). We show how these metrics give valuable insight into the selection of the appropriate IDS architecture for an AMI network.","abstract_has_math":false,"creators":["Rausch, Michael J."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sanders, William H."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:49:35Z","date_published":"2017-03-01T15:49:35Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Adversary view security evaluation (ADVISE)","Advanced metering infrastructure (AMI)","Model","Intrusion detection system (IDS)"],"languages":["en"],"rights":["Copyright 2016 Michael J. Rausch"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95424","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sanders, William H."]},{"key":"dc:creator","label":"Author","values":["Rausch, Michael J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:49:35Z","2016-12-08","2016-12"]},{"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":["Adversary view security evaluation (ADVISE)","Advanced metering infrastructure (AMI)","Model","Intrusion detection system (IDS)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Michael J. Rausch"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95424"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Utilities responsible for Advanced Metering Infrastructure (AMI) networks must be able to defend themselves from a variety of potential attacks so they may achieve the goals of delivering power to consumers and maintaining the integrity of their equipment and data. Intrusion detection systems (IDSes) can play an important part in the defense of such networks. Utilities should carefully consider the strengths and weaknesses of different IDS deployment strategies to choose the most cost-effective solution. Models of adversary behavior in the presence of different IDS deployments can help with making this decision as we demonstrate through a case study that uses a model created in the ADversary VIew Security Evaluation (ADVISE) formalism (which calculates metrics used to compare different IDSes). We show how these metrics give valuable insight into the selection of the appropriate IDS architecture for an AMI network.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Michael Rausch, accepted the attached license on 2016-12-08 at 16:09.","The student, Michael Rausch, submitted this Thesis for approval on 2016-12-08 at 16:14.","This Thesis was approved for publication on 2016-12-08 at 16:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10496 on 2017-02-28 at 15:03:54","Made available in DSpace on 2017-03-01T15:49:35Z (GMT). No. of bitstreams: 3 RAUSCH-THESIS-2016.pdf: 7294429 bytes, checksum: a5ada063e5410a0cf94db1eecffc944a (MD5) SourceFiles.zip: 23480216 bytes, checksum: 3997db28ef21d37fbf465a36de90d36f (MD5) LICENSE.txt: 4211 bytes, checksum: ebb4272bc88781d37d127699af386d17 (MD5) Previous issue date: 2016-12-08"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Determining cost-effective intrusion detection approaches for an advanced metering infrastructure deployment using advise"]}]}],"canonical_facts":{"dc:contributor":["Sanders, William H."],"dc:creator":["Rausch, Michael J."],"dc:date":["2017-03-01T15:49:35Z","2016-12-08","2016-12"],"dc:description":["Utilities responsible for Advanced Metering Infrastructure (AMI) networks must be able to defend themselves from a variety of potential attacks so they may achieve the goals of delivering power to consumers and maintaining the integrity of their equipment and data. Intrusion detection systems (IDSes) can play an important part in the defense of such networks. Utilities should carefully consider the strengths and weaknesses of different IDS deployment strategies to choose the most cost-effective solution. Models of adversary behavior in the presence of different IDS deployments can help with making this decision as we demonstrate through a case study that uses a model created in the ADversary VIew Security Evaluation (ADVISE) formalism (which calculates metrics used to compare different IDSes). We show how these metrics give valuable insight into the selection of the appropriate IDS architecture for an AMI network.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Michael Rausch, accepted the attached license on 2016-12-08 at 16:09.","The student, Michael Rausch, submitted this Thesis for approval on 2016-12-08 at 16:14.","This Thesis was approved for publication on 2016-12-08 at 16:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10496 on 2017-02-28 at 15:03:54","Made available in DSpace on 2017-03-01T15:49:35Z (GMT). No. of bitstreams: 3 RAUSCH-THESIS-2016.pdf: 7294429 bytes, checksum: a5ada063e5410a0cf94db1eecffc944a (MD5) SourceFiles.zip: 23480216 bytes, checksum: 3997db28ef21d37fbf465a36de90d36f (MD5) LICENSE.txt: 4211 bytes, checksum: ebb4272bc88781d37d127699af386d17 (MD5) Previous issue date: 2016-12-08"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/95424"],"dc:language":["en"],"dc:rights":["Copyright 2016 Michael J. Rausch"],"dc:subject":["Adversary view security evaluation (ADVISE)","Advanced metering infrastructure (AMI)","Model","Intrusion detection system (IDS)"],"dc:title":["Determining cost-effective intrusion detection approaches for an advanced metering infrastructure deployment using advise"],"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":"2026-07-22T22:26:37Z"}