Committee Chair

Xie, Mengjun

Committee Member

Yang, Li; Qin, Hong ;Wang, Jin

Department

Dept. of Computational Science

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

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.

Acknowledgments

I would like to express my heartfelt gratitude to Dr. Mengjun Xie for being an outstanding academic advisor and a cherished friend. Your unwavering guidance and support have been instrumental in helping me navigate the challenging waters of my Ph.D. journey. I have learned a lot from you, both academically and about American culture, and I hope we can continue our collaboration in the future. My sincere thanks also go to my fellow NexusLab members for providing support and encouragement to each other during the past few years. Additionally, I would like to thank Ms. Kim Sapp, Ms. Lora Cook, and many others who provided invaluable support to international students like me. Your assistance has been crucial in helping me stay on track and fulfilling the requirements of the Ph.D. program. My appreciation also goes to MDRB and CSE faculty and stuff for their teaching and research support. Finally, I would like to extend my thanks to Dr. Li Yang, Dr. Hong Qin, and Dr. Jin Wang for serving as members of my dissertation committee. Your invaluable insights and feedback have been extremely helpful, and I am deeply grateful for your contributions.

Degree

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.

Date

5-2023

Subject

Internet of things; Blockchains (Databases); Digital forensic science

Keyword

Internet of Things; digital forensics; knowledge graph; ontology design; question answering

Discipline

Artificial Intelligence and Robotics

Document Type

Doctoral dissertations

DCMI Type

Text

Extent

xviii, 130 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

Date Available

5-31-2024

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Available for download on Friday, May 31, 2024

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