{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1732"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1732","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Advanced threat hunting over software-defined networks in smart cities","abstract":"The emergence of Software-Defined Networking (SDN) has brought along a wave of new technologies and developments in the field of networking with hopes of dealing with network resources more efficiently and providing a foundation of programmability. SDN allows for both flexibility and adaptability by separating the control and data planes in a network environment by virtualizing network hardware. Threat hunting is a technique that allows for the detection of advanced network threats through forensic analysis. We present an advanced threat hunting model by combining the SDN infrastructure with threat hunting techniques and machine learning models aiming to intelligently handle advanced network threats such as lateral movement. We found that our approach outperforms current threat hunting models in vital areas such as the detection to mitigation time. Our results show that we are able to detect advanced threats with 93.4% accuracy and begin mitigation within 10 seconds of detection.","abstract_html":"The emergence of Software-Defined Networking (SDN) has brought along a wave of new technologies and developments in the field of networking with hopes of dealing with network resources more efficiently and providing a foundation of programmability. SDN allows for both flexibility and adaptability by separating the control and data planes in a network environment by virtualizing network hardware. Threat hunting is a technique that allows for the detection of advanced network threats through forensic analysis. We present an advanced threat hunting model by combining the SDN infrastructure with threat hunting techniques and machine learning models aiming to intelligently handle advanced network threats such as lateral movement. We found that our approach outperforms current threat hunting models in vital areas such as the detection to mitigation time. Our results show that we are able to detect advanced threats with 93.4% accuracy and begin mitigation within 10 seconds of detection.","abstract_has_math":false,"creators":["Schmitt, Steven"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Kandah, Farah","Yang, Li; Skjellum, Anthony","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:46:51Z","subjects":["Software-defined networking (Computer network technology)","Computational intelligence","Computer networks -- Security measures"],"languages":["English","eng"],"rights":[],"rights_urls":["https://rightsstatements.org/page/InC/1.0/?language=en"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/576","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kandah, Farah","Yang, Li; Skjellum, Anthony","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Schmitt, Steven"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-12-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Software-defined networking (Computer network technology)","Computational intelligence","Computer networks -- Security measures"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://rightsstatements.org/page/InC/1.0/?language=en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/576"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["The emergence of Software-Defined Networking (SDN) has brought along a wave of new technologies and developments in the field of networking with hopes of dealing with network resources more efficiently and providing a foundation of programmability. SDN allows for both flexibility and adaptability by separating the control and data planes in a network environment by virtualizing network hardware. Threat hunting is a technique that allows for the detection of advanced network threats through forensic analysis. We present an advanced threat hunting model by combining the SDN infrastructure with threat hunting techniques and machine learning models aiming to intelligently handle advanced network threats such as lateral movement. We found that our approach outperforms current threat hunting models in vital areas such as the detection to mitigation time. Our results show that we are able to detect advanced threats with 93.4% accuracy and begin mitigation within 10 seconds of detection."]},{"key":"dc:title","label":"Title","values":["Advanced threat hunting over software-defined networks in smart cities"]}]}],"canonical_facts":{"dc:contributor":["Kandah, Farah","Yang, Li; Skjellum, Anthony","College of Engineering and Computer Science"],"dc:creator":["Schmitt, Steven"],"dc:date":["2018-12-01T08:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","M. 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We found that our approach outperforms current threat hunting models in vital areas such as the detection to mitigation time. Our results show that we are able to detect advanced threats with 93.4% accuracy and begin mitigation within 10 seconds of detection."],"dc:identifier":["https://scholar.utc.edu/theses/576"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["https://rightsstatements.org/page/InC/1.0/?language=en"],"dc:subject":["Software-defined networking (Computer network technology)","Computational intelligence","Computer networks -- Security measures"],"dc:title":["Advanced threat hunting over software-defined networks in smart cities"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:46:51Z"}