{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/889"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/889","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Using detection in depth to counter SCADA-specific advanced persistent threats","abstract":"A heavy focus has recently been placed on the current state of each country’s critical infrastructure security. Unfortunately, widely deployed supervisory control and data acquisition (SCADA) protocols provide little to no inherent security controls while traditional security mechanisms prove largely ineffective in industrial control environments. Moreover, the recent advent of advanced persistent threats (APTs) has highlighted the relative ineffectiveness of existing SCADA-centric security solutions. In this thesis I will identify various algorithmic strategies for detecting and mitigating common APT attack vectors impacting SCADA environments. Primarily, the integration of flow-based intrusion detection systems, passive device fingerprinting, low- interaction honeypots, and traditional signature- based intrusion detection technologies provides a highly effective capacity for detecting common attack vectors used by APTs. Finally I will show how the integration of these technologies into a single security solution has provided a verifiably robust and effective solution for the problem at hand.","abstract_html":"A heavy focus has recently been placed on the current state of each country’s critical infrastructure security. Unfortunately, widely deployed supervisory control and data acquisition (SCADA) protocols provide little to no inherent security controls while traditional security mechanisms prove largely ineffective in industrial control environments. Moreover, the recent advent of advanced persistent threats (APTs) has highlighted the relative ineffectiveness of existing SCADA-centric security solutions. In this thesis I will identify various algorithmic strategies for detecting and mitigating common APT attack vectors impacting SCADA environments. Primarily, the integration of flow-based intrusion detection systems, passive device fingerprinting, low- interaction honeypots, and traditional signature- based intrusion detection technologies provides a highly effective capacity for detecting common attack vectors used by APTs. Finally I will show how the integration of these technologies into a single security solution has provided a verifiably robust and effective solution for the problem at hand.","abstract_has_math":false,"creators":["Hayes, Garrett"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["El-Khatib, Khalil"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-04-01","date_published":"2014-04-01","updated_at":"2026-07-24T05:35:38Z","subjects":["Industrial control security","SCADA security","Advanced persistent threats","Intrusion detection","Intrusion prevention","Critical infrastructure security"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/889","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["El-Khatib, Khalil"]},{"key":"dc:creator","label":"Author","values":["Hayes, Garrett"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-12T16:29:51Z","2022-03-29T17:39:20Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-12T16:29:51Z","2022-03-29T17:39:20Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Industrial control security","SCADA security","Advanced persistent threats","Intrusion detection","Intrusion prevention","Critical infrastructure security"]}]},{"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/10155/889"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A heavy focus has recently been placed on the current state of each country’s critical infrastructure security. Unfortunately, widely deployed supervisory control and data acquisition (SCADA) protocols provide little to no inherent security controls while traditional security mechanisms prove largely ineffective in industrial control environments. Moreover, the recent advent of advanced persistent threats (APTs) has highlighted the relative ineffectiveness of existing SCADA-centric security solutions. In this thesis I will identify various algorithmic strategies for detecting and mitigating common APT attack vectors impacting SCADA environments. Primarily, the integration of flow-based intrusion detection systems, passive device fingerprinting, low- interaction honeypots, and traditional signature- based intrusion detection technologies provides a highly effective capacity for detecting common attack vectors used by APTs. Finally I will show how the integration of these technologies into a single security solution has provided a verifiably robust and effective solution for the problem at hand."]},{"key":"dc:title","label":"Title","values":["Using detection in depth to counter SCADA-specific advanced persistent threats"]}]}],"canonical_facts":{"dc:contributor.advisor":["El-Khatib, Khalil"],"dc:creator":["Hayes, Garrett"],"dc:date.accessioned":["2018-01-12T16:29:51Z","2022-03-29T17:39:20Z"],"dc:date.available":["2018-01-12T16:29:51Z","2022-03-29T17:39:20Z"],"dc:date.issued":["2014-04-01"],"dc:description.abstract":["A heavy focus has recently been placed on the current state of each country’s critical infrastructure security. Unfortunately, widely deployed supervisory control and data acquisition (SCADA) protocols provide little to no inherent security controls while traditional security mechanisms prove largely ineffective in industrial control environments. Moreover, the recent advent of advanced persistent threats (APTs) has highlighted the relative ineffectiveness of existing SCADA-centric security solutions. In this thesis I will identify various algorithmic strategies for detecting and mitigating common APT attack vectors impacting SCADA environments. Primarily, the integration of flow-based intrusion detection systems, passive device fingerprinting, low- interaction honeypots, and traditional signature- based intrusion detection technologies provides a highly effective capacity for detecting common attack vectors used by APTs. Finally I will show how the integration of these technologies into a single security solution has provided a verifiably robust and effective solution for the problem at hand."],"dc:identifier.uri":["https://hdl.handle.net/10155/889"],"dc:language.iso":["en"],"dc:subject":["Industrial control security","SCADA security","Advanced persistent threats","Intrusion detection","Intrusion prevention","Critical infrastructure security"],"dc:title":["Using detection in depth to counter SCADA-specific advanced persistent threats"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:38Z"}