[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119928-en":3,"doc-seo-119928-105":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":20,"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},119928,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Cost-Optimized Dynamic Access Control Policy Using Blockchain and Machine Learning for Enhanced Security in IoT Smart Homes - Research Paper","Rapid adoption of Internet of Things (IoT) devices in smart homes has exposed security weaknesses in traditional static access control approaches. This paper proposes a cost-optimized, dynamic access control policy that combines blockchain immutability and transparency (Ethereum) with machine learning. Support Vector Machines (SVM) and Neural Networks analyze user and device behaviors to detect evolving threats in real time. A caching mechanism on Ethereum reduces latency and improves efficiency. Experimental evaluations show stronger access control security, adaptability, and system performance, supporting scalable IoT security research.","Cost-Optimized Dynamic Access Control Policy Using Blockchain and Machine Learning for Enhanced Security in IoT Smart Homes  \nHafiz Adnan Hussain, Zulkefli Mansor, Zarina Shukur, and Uzma Jafar  \nUniversiti Kebangsaan Malaysia (UKM), Malaysia  \nAbstract. The rapid adoption of Internet of Things (IoT) devices in smart homes has led to growing security vulnerabilities, primarily due to the limitations of traditional, static access control mechanisms. This paper presents a novel, dynamic access control policy that leverages the immutable and transparent nature of Blockchain technology, specifically Ethereum, along with machine learning algorithms to enhance security measures. By integrating machine learning algorithms like Support Vector Machines (SVM) and Neural Networks, the proposed system can adapt and respond to changing behavioural patterns and potential threats in real time.  \nAdditionally, a caching mechanism implemented on the Ethereum Blockchain is introduced to optimize system performance and reduce latency. Experimental results demonstrate significant improvements in access control security, system efficiency, and adaptability. The findings of this paper not only contribute to the advancement of secure access control policies for IoT smart homes but pave the way for future research in integrating Blockchain and machine learning for robust and scalable IoT  \nsecurity solutions.  \nKeywords: Cost Optimization, Security, Internet of Things (IoT), Access Control, Blockchain, Artificial Intelligence, Machine Learning, Cache, Storage  \n1 Introduction  \nThe Internet of Things (IoT) is transforming how we interact with our environment, particularly within the confines of our homes. Smart homes, equipped with an array of interconnected devices, offer an unprecedented level of convenience and automation.  \nHowever, this interconnectivity poses significant security challenges, especially concerning access control [1, 2] . Traditional access control mechanisms, such as Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), are primarily static. They are predefined and do not adapt to evolving behavioural patterns or emerging threats, making them susceptible to various forms of cyber-attacks [3] . The primary objective of this research is to develop a dynamic access control system that can adapt to changing conditions in realtime. The system will leverage Blockchain technology for its immutable and transparent nature, ensuring that access logs are tamper proof [4, 5] . It will also incorporate machine learning algorithms to analyse device behaviour and user interaction patterns, making the  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nsystem adaptive to new situations [6, 7] . This paper contributes to the field by proposing novel algorithms for dynamic access control in IoT smart homes. These algorithms are integrated with a caching mechanism built on Ethereum, which enhances system efficiency without compromising security[8, 9] . The research validates these algorithms through rigorous experimental setups, comparing them against existing mechanisms to highlight their efficacy and adaptability.  \n2 Related Work  \nThe advent of advanced computational technologies has opened up new avenues for enhancing security in the ever-expanding domain of the Internet of Things (IoT) . This section surveys the pertinent literature on blockchain and machine learning (ML), which are the cornerstones of this research. Blockchain technology first conceptualized as the underlying framework for digital currencies, has transcended its initial application to emerge as a revolutionary tool for secure, decentralized consensus and record-keeping. Its intrinsic properties of transparency, immutability, and resistance to tampering are pa","cbCaifHLXLZ8KRey","https://ap.wps.com/l/cbCaifHLXLZ8KRey","pdf",434071,1,6,"English","en",105,"# 1 Introduction\n# 2 Related Work\n# 3 Methodology\n## 3.1 Dynamic Access Control in IoT","[{\"question\":\"What problem does the proposed system address in IoT smart homes?\",\"answer\":\"It addresses security vulnerabilities caused by traditional static access control mechanisms that cannot adapt to changing behaviors or emerging threats.\"},{\"question\":\"How does blockchain contribute to access control in the proposed policy?\",\"answer\":\"It leverages Ethereum’s immutable and transparent properties to make access logs tamper-proof and support trustworthy record-keeping.\"},{\"question\":\"Which machine learning methods are used, and what is their purpose?\",\"answer\":\"The system uses Support Vector Machines (SVM) and Neural Networks to analyze user interaction and device behavior patterns, enabling real-time adaptation to potential threats.\"}]","Cost-Optimized Dynamic Access Control Policy Using Blockchain and Machine Learning for Enhanced Security in IoT Smart Homes - 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