{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1965"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1965","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Advancing IoT security through blockchain-based decentralized platform and AI-powered digital forensics","abstract":"The proliferation of Internet of Things (IoT) devices, from smartphones, smart thermostats to smart home security systems, is revolutionizing our society and daily lives. However, it also has posed significant challenges to IoT security and forensics. To tackle those challenges, innovative solutions are designed to enhancing IoT security and accelerating investigation of cybersecurity incidents by leveraging recent technological advancements in Blockchain and Artificial Intelligence (AI). First, an IoT service platform, called DISP, is proposed to improve the security and interoperability of IoT systems. DISP utilizes the consortium blockchain technology to transform centralized, insecure IoT communications into decentralized, secure, and traceable IoT services. Second, novel techniques are developed to tackle the new challenges in IoT forensics. To address scalability and data volatility challenges in large-scale mobile forensics, a scalable remote live forensics system, named ReLF, is designed and developed for live extraction of digital artifacts from Android devices. To address the heterogeneity and volume of IoT data in forensics, a novel Knowledge Graph Question Answering (KGQA) framework for processing and analyzing forensic data is proposed. This framework provides an intuitive interface for cybersecurity and forensic professionals to access and analyze evidence using natural language-based questions. By creating those systems for strengthening IoT security and facilitating IoT forensics, this research shines a light on new directions in and approaches to securing IoT systems and investigating cyberattacks against those systems, and it lays the foundation for integrating IoT security with emerging technologies especially blockchain and artificial intelligence.","abstract_html":"The proliferation of Internet of Things (IoT) devices, from smartphones, smart thermostats to smart home security systems, is revolutionizing our society and daily lives. However, it also has posed significant challenges to IoT security and forensics. To tackle those challenges, innovative solutions are designed to enhancing IoT security and accelerating investigation of cybersecurity incidents by leveraging recent technological advancements in Blockchain and Artificial Intelligence (AI). First, an IoT service platform, called DISP, is proposed to improve the security and interoperability of IoT systems. DISP utilizes the consortium blockchain technology to transform centralized, insecure IoT communications into decentralized, secure, and traceable IoT services. Second, novel techniques are developed to tackle the new challenges in IoT forensics. To address scalability and data volatility challenges in large-scale mobile forensics, a scalable remote live forensics system, named ReLF, is designed and developed for live extraction of digital artifacts from Android devices. To address the heterogeneity and volume of IoT data in forensics, a novel Knowledge Graph Question Answering (KGQA) framework for processing and analyzing forensic data is proposed. This framework provides an intuitive interface for cybersecurity and forensic professionals to access and analyze evidence using natural language-based questions. By creating those systems for strengthening IoT security and facilitating IoT forensics, this research shines a light on new directions in and approaches to securing IoT systems and investigating cyberattacks against those systems, and it lays the foundation for integrating IoT security with emerging technologies especially blockchain and artificial intelligence.","abstract_has_math":false,"creators":["Zhang, Ruipeng"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Xie, Mengjun","Yang, Li; Qin, Hong ;Wang, Jin","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-31T07:00:00Z","date_published":"2024-05-31T07:00:00Z","updated_at":"2026-07-24T05:47:06Z","subjects":["Internet of things","Blockchains (Databases)","Digital forensic science"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/787","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Xie, Mengjun","Yang, Li; Qin, Hong ;Wang, Jin","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Zhang, Ruipeng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-05-31T07:00:00Z"]},{"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":["Doctoral dissertations","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Internet of things","Blockchains (Databases)","Digital forensic science"]}]},{"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/787"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computational Science","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."]},{"key":"dc:description.abstract","label":"Abstract","values":["The proliferation of Internet of Things (IoT) devices, from smartphones, smart thermostats to smart home security systems, is revolutionizing our society and daily lives. However, it also has posed significant challenges to IoT security and forensics. To tackle those challenges, innovative solutions are designed to enhancing IoT security and accelerating investigation of cybersecurity incidents by leveraging recent technological advancements in Blockchain and Artificial Intelligence (AI). First, an IoT service platform, called DISP, is proposed to improve the security and interoperability of IoT systems. DISP utilizes the consortium blockchain technology to transform centralized, insecure IoT communications into decentralized, secure, and traceable IoT services. Second, novel techniques are developed to tackle the new challenges in IoT forensics. To address scalability and data volatility challenges in large-scale mobile forensics, a scalable remote live forensics system, named ReLF, is designed and developed for live extraction of digital artifacts from Android devices. To address the heterogeneity and volume of IoT data in forensics, a novel Knowledge Graph Question Answering (KGQA) framework for processing and analyzing forensic data is proposed. This framework provides an intuitive interface for cybersecurity and forensic professionals to access and analyze evidence using natural language-based questions. By creating those systems for strengthening IoT security and facilitating IoT forensics, this research shines a light on new directions in and approaches to securing IoT systems and investigating cyberattacks against those systems, and it lays the foundation for integrating IoT security with emerging technologies especially blockchain and artificial intelligence."]},{"key":"dc:title","label":"Title","values":["Advancing IoT security through blockchain-based decentralized platform and AI-powered digital forensics"]}]}],"canonical_facts":{"dc:contributor":["Xie, Mengjun","Yang, Li; Qin, Hong ;Wang, Jin","College of Engineering and Computer Science"],"dc:creator":["Zhang, Ruipeng"],"dc:date":["2023-05-01T07:00:00Z"],"dc:date.available":["2024-05-31T07:00:00Z"],"dc:description":["Dept. of Computational Science","Ph. 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Second, novel techniques are developed to tackle the new challenges in IoT forensics. To address scalability and data volatility challenges in large-scale mobile forensics, a scalable remote live forensics system, named ReLF, is designed and developed for live extraction of digital artifacts from Android devices. To address the heterogeneity and volume of IoT data in forensics, a novel Knowledge Graph Question Answering (KGQA) framework for processing and analyzing forensic data is proposed. This framework provides an intuitive interface for cybersecurity and forensic professionals to access and analyze evidence using natural language-based questions. By creating those systems for strengthening IoT security and facilitating IoT forensics, this research shines a light on new directions in and approaches to securing IoT systems and investigating cyberattacks against those systems, and it lays the foundation for integrating IoT security with emerging technologies especially blockchain and artificial intelligence."],"dc:identifier":["https://scholar.utc.edu/theses/787"],"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":["Internet of things","Blockchains (Databases)","Digital forensic science"],"dc:title":["Advancing IoT security through blockchain-based decentralized platform and AI-powered digital forensics"],"dc:type":["Doctoral dissertations","Text"]},"updated_at":"2026-07-24T05:47:06Z"}