{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1838"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1838","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Behavioral model based trust management design for IoT at scale","abstract":"With the rise in the number of devices in the Internet of Things (IoT), the number of malicious devices will also drastically increase. Smart cities' decisions are based on data being collected by IoT devices in real-time, of which a connected-vehicle system is included. Behaviors such as malicious data injection can significantly impact connected vehicles. To aid in combating this threat, monitoring smart city and connected vehicle's sensor data will allow for construction of a behavioral model. Implementing machine learning will aid in constructing a standard behavior such that any device that begins to malfunction or behave maliciously can be detected and mitigated in real-time. This behavioral analysis will be further applied to supplement trust management approaches such that a more accurate value can be associated with the device's perceived trustworthiness without the need to rely on a majority consensus.","abstract_html":"With the rise in the number of devices in the Internet of Things (IoT), the number of malicious devices will also drastically increase. Smart cities' decisions are based on data being collected by IoT devices in real-time, of which a connected-vehicle system is included. Behaviors such as malicious data injection can significantly impact connected vehicles. To aid in combating this threat, monitoring smart city and connected vehicle's sensor data will allow for construction of a behavioral model. Implementing machine learning will aid in constructing a standard behavior such that any device that begins to malfunction or behave maliciously can be detected and mitigated in real-time. This behavioral analysis will be further applied to supplement trust management approaches such that a more accurate value can be associated with the device's perceived trustworthiness without the need to rely on a majority consensus.","abstract_has_math":false,"creators":["Huber, Brennan"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Kandah, Farah","Skjellum, Anthony; Tanis, Craig; Ward, Michael","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:46:59Z","subjects":["Computer security","Internet of things","Machine learning","Smart cities","Vehicular ad hoc networks (Computer networks)"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/675","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kandah, Farah","Skjellum, Anthony; Tanis, Craig; Ward, Michael","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Huber, Brennan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-01T07:00:00Z"]},{"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":["Computer security","Internet of things","Machine learning","Smart cities","Vehicular ad hoc networks (Computer networks)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/675"]}]},{"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":["With the rise in the number of devices in the Internet of Things (IoT), the number of malicious devices will also drastically increase. Smart cities' decisions are based on data being collected by IoT devices in real-time, of which a connected-vehicle system is included. Behaviors such as malicious data injection can significantly impact connected vehicles. To aid in combating this threat, monitoring smart city and connected vehicle's sensor data will allow for construction of a behavioral model. Implementing machine learning will aid in constructing a standard behavior such that any device that begins to malfunction or behave maliciously can be detected and mitigated in real-time. This behavioral analysis will be further applied to supplement trust management approaches such that a more accurate value can be associated with the device's perceived trustworthiness without the need to rely on a majority consensus."]},{"key":"dc:title","label":"Title","values":["Behavioral model based trust management design for IoT at scale"]}]}],"canonical_facts":{"dc:contributor":["Kandah, Farah","Skjellum, Anthony; Tanis, Craig; Ward, Michael","College of Engineering and Computer Science"],"dc:creator":["Huber, Brennan"],"dc:date":["2020-08-01T07:00:00Z"],"dc:description":["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."],"dc:description.abstract":["With the rise in the number of devices in the Internet of Things (IoT), the number of malicious devices will also drastically increase. Smart cities' decisions are based on data being collected by IoT devices in real-time, of which a connected-vehicle system is included. Behaviors such as malicious data injection can significantly impact connected vehicles. To aid in combating this threat, monitoring smart city and connected vehicle's sensor data will allow for construction of a behavioral model. Implementing machine learning will aid in constructing a standard behavior such that any device that begins to malfunction or behave maliciously can be detected and mitigated in real-time. This behavioral analysis will be further applied to supplement trust management approaches such that a more accurate value can be associated with the device's perceived trustworthiness without the need to rely on a majority consensus."],"dc:identifier":["https://scholar.utc.edu/theses/675"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Computer security","Internet of things","Machine learning","Smart cities","Vehicular ad hoc networks (Computer networks)"],"dc:title":["Behavioral model based trust management design for IoT at scale"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:46:59Z"}