[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123029-en":3,"doc-seo-123029-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123029,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Lightweight Machine-Learning Framework for Enhancing Security in Iot Blockchain Networks - Doctoral Dissertation","Blockchain technology accelerates adoption but faces security bottlenecks driven by computationally intensive defenses for spam resistance and conflict resolution. This dissertation investigates a machine-learning–enabled blockchain direction that supports limited nodes such as IoT devices, enabling independent decisions using compressed knowledge of prior blockchain history. Security threats introduced by machine-intelligence are analyzed with theoretical proofs and implementations in realistic IoT blockchain environments. Proposed components include evasion-defense mechanisms, distributed fraud-learning using lightweight GANs, reputation and consensus schemes for robust transaction finalization, and lightweight white-box optimization for auditing with machine-learning.","Scholars' Mine  \n\n| Doctoral Dissertations | Student Theses and Dissertations |\n| --- | --- |\n| Summer 2024\u003Cbr>A Lightweight Machine-Learning Framework for Enhancing Security in Iot Blockchain Networks\u003Cbr>Charles Connor Rawlins\u003Cbr>Missouri University of Science and Technology\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/doctoral_dissertations](https://scholarsmine.mst.edu/doctoral_dissertations)\u003Cbr> Part of the Computer Engineering Commons\u003Cbr>Department: Electrical and Computer Engineering |  |\n\nRecommended Citation  \nRawlins, Charles Connor, \"A Lightweight Machine-Learning Framework for Enhancing Security in Iot Blockchain Networks\" (2024) . Doctoral Dissertations. 3323.  \n[https://scholarsmine.mst.edu/doctoral_dissertations/3323](https://scholarsmine.mst.edu/doctoral_dissertations/3323)  \nThis thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nA LIGHTWEIGHT MACHINE-LEARNING FRAMEWORK FOR ENHANCING SECURITY IN IOT BLOCKCHAIN NETWORKS  \nby  \nCHARLES CONNOR RAWLINS  \nA DISSERTATION  \nPresented to the Graduate Faculty of the  \nMISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY  \nIn Partial Fulfillment of the Requirements for the Degree  \nDOCTOR OF PHILOSOPHY  \nin  \nELECTRICAL AND COMPUTER ENGINEERING  \n2024  \nApproved by:  \nDr. Jagannathan Sarangapani, Advisor  \nDr. Maciej Zawodniok  \nDr. Sahra Sedighsarvestani  \nDr. Tony Luo  \nDr. Sanjay Madria  \nCopyright 2024  \nCHARLES CONNOR RAWLINS  \nAll Rights Reserved  \niii  \nPUBLICATION DISSERTATION OPTION  \nThis dissertation consists of the following five papers which have been submitted for publication following the formatting style for Missouri University of Science and Technology:  \nPaper I: An Intelligent Distributed Ledger Construction Algorithm for IoT, found in pages 13-58, was published by IEEE Access.  \nPaper II: Predicting IoT Distributed Ledger Fraud Transactions with a Lightweight GAN Network, found in pages 59-108, was published by IEEE Transactions on Mobile Computing.  \nPaper III: A Reputation System for Provably-robust Decision-making in IoT Blockchain Networks, found in pages 109-161, has been accepted by IEEE Internet of Things Journal.  \nPaper IV: Secure Q-learning Exploration Consensus for Intelligent Decisionmaking Against Naive Attacks in IoT Blockchain Networks, pages 162- 210 has been submitted to IEEE Transactions on Mobile Computing.  \nPaper V: Towards Fuzzy Decision Tree Feedback Alignment in Intelligent IoT Blockchain Networks, found in pages 211-250, has been submitted to IEEE International Conference on Blockchain and Cryptocurrency (ICBC) 2024 .  \niv  \nABSTRACT  \nBlockchain is one of the fastest technologies that rivals the Internet in terms ofadoption speed. This security method is applicable to data-centric environments for validating data in the presence of faults. However, traditional blockchain implementation introduces bottlenecks with computationally-intense security measures to prevent malicious spam and resolve conflicts. This dissertation explores a new direction for blockchain technology that allows limited nodes, like Internet of Things (IoT) devices, to make independant decisions with compressed knowledge of past blockchain history through the use of machine-learning for active decisions (or, the first machine-intelligent blockchain protocol) . Proposing to introduce machine-intelligence into the rapidly-evolving paradigm creates unique security challenges, which this dissertation addresses. Each effort explored was analyzed for both its theoretical proofs and implemented in a realistic IoT blockchain environment.  \nThe dissertation effort is organized into several sections. The first section addresses blockchain-empowered evasion att","cbCaifGNvUaLTtdM","https://ap.wps.com/l/cbCaifGNvUaLTtdM","pdf",10188178,1,308,"English","en",105,"# ABSTRACT\n## Blockchain and machine-intelligence motivation\n## Dissertation structure and research contributions","[{\"question\":\"What problem does the dissertation target in blockchain security?\",\"answer\":\"It targets the security bottlenecks created by traditional blockchain defenses that rely on computationally intensive measures for spam prevention and conflict resolution.\"},{\"question\":\"How does the work enable limited IoT nodes to participate in blockchain decisions?\",\"answer\":\"It introduces machine-learning–based active decision mechanisms so limited nodes can make independent decisions using compressed knowledge of past blockchain history.\"},{\"question\":\"What are the main technical components proposed in the dissertation?\",\"answer\":\"The dissertation develops multiple efforts including reward-function and robust multiarm bandit defenses, distributed learning for fraud transactions using a lightweight GAN, a reputation system with game-theoretic protection, a consensus scheme for transaction finalization, and a lightweight auditing optimization for white-box machine-learning.\"}]","A Lightweight Machine-Learning Framework for Enhancing Security in Iot Blockchain Networks - 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