[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121563-en":3,"doc-seo-121563-105":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},121563,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","IMPROVING 5G NETWORK SECURITY USING MACHINE LEARNING WITH MQTT DATA ANALYSIS - Master’s Thesis","Rapid proliferation of Internet of Things (IoT) devices and global adoption of 5G increase the need for stronger security mechanisms for connected systems. This master’s thesis studies user security in 5G networks with emphasis on the Message Queuing Telemetry Transport (MQTT) protocol, widely used for lightweight IoT messaging. Using the MQTT-IoT-IDS2020 dataset, advanced machine learning models detect and predict intrusions, and the proposed Catboost-based approach achieves 99.99% accuracy, outperforming prior benchmarks. Results demonstrate that optimized machine learning can significantly enhance security and reliability of 5G-connected IoT devices and support future research.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY Institute of Graduate Studies Electrical and Computer Engineering  \nIMPROVING 5G NETWORK SECURITY USING MACHINE LEARNING WITH MQTT  \nDATA ANALYSIS  \nMuntadher Sameer Jameel AL-IBADI  \nMaster ’s Thesis  \nSupervisor  \nAsst. Prof. Dr. Abdullahi Abdu IBRAHIM  \nİstanbul, 2024  \nIMPROVING 5G NETWORK SECURITY USING MACHINE LEARNING WITH MQTT DATA ANALYSIS  \nMuntadher Sameer Jameel AL-IBADI  \nElectrical and Computer Engineering  \nMaster’s Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled Fractures Decay and Crack Detection in Oil Pipelines Using Deep Convolutional Neural Network prepared by Muntadher Sameer Jameel AL-IBADI and submitted on 02/01/2024 has been accepted unanimously for the degree of Master of Science in Electrical and Computer Engineering.  \nAsst. Prof. Dr. Abdullhi Abdu IBRAHIM  \nSupervisor  \nThesis Defense Committee Members:  \nAsst. Prof. Dr. Abdullhi Abdu  \nIBRAHIM  \nAssoc. Prof. Dr. Sefer KURNAZ  \nDepartment of Software  \nEngineering,  \nAltınbaş University  \nDepartment of Computer  \nEngineering,  \nAltınbaş University  \n__________________  \n__________________  \nAsst. Prof. Dr. Tariq Abed  \nMOHAMMED  \nDepartment of Information Technologies,  \nKirkuk University    \nI hereby declare that this thesis meets all format and submission requirements ofa Master’s thesis.  \nSubmission date of the thesis to Institute of Graduate Studies:  / /   \nI hereby declare that all information/data presented in this graduation project has been obtained in full accordance with academic rules and ethical conduct. I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and vice versa as required by the abovementioned rules and conduct.  \nMuntadher Sameer Jameel AL-IBADI  \nSignature  \nDEDICATION  \nTo my distinguished teacher, Dr. Abdullahi Abdu IBRAHIM, who enlightened my academic journey. Your unwavering support has been crucial in crafting this dissertation, and I am deeply grateful for your commitment to academic excellence. To my family who supported my perseverance, and to the students of science, I dedicate my humble effort.  \nABSTRACT  \nIMPROVING 5G NETWORK SECURITY USING MACHINE LEARNING WITH MQTT DATA ANALYSIS  \nAL-IBADI, Muntadher Sameer Jameel  \nM.Sc., Electrical and Computer Engineering, Altınbaş University,  \nSupervisor: Asst. Prof. Dr. Abdullahi Abdu IBRAHIM  \nDate: January / 2024  \nPages: 59  \nWith the rapid proliferation of Internet of Things (IoT) devices and the global shift towards 5G technology, there arises a critical need to address and bolster the security mechanisms of these systems. This thesis presents a comprehensive study on user security within 5G networks, specifically focusing on the Message Queuing Telemetry Transport (MQTT) protocol, a widely adopted lightweight messaging protocol in IoT environments. Utilizing the MQTT-IoT-IDS2020 dataset, we employed advanced machine learning models to detect and predict potential intrusions. Our proposed model, based on the Catboost algorithm, showcases superior performance with an accuracy rate of 99.99%, outpacing previously established benchmarks. This work not only emphasizes the vulnerabilities present in modern 5G IoT networks but also demonstrates the efficacy of machine learning as a tool to counteract these challenges. The results highlight the potential for machine learning algorithms, when optimized and appropriately applied, to significantly enhance the security and reliability of 5G-connected IoT devices. Furthermore, the study sets the foundation for future research endeavors aiming to solidify security in our increasingly connected digital landscape.  \nKeywords: 5G Networks, Internet of Things (IoT), Message Queuing Telemetry Transport (MQTT) Protocol, Intrusion Detection System, Machine Learning, Cybersecurity.  \nTABLE OF CONTENTS  \nPages  \nABSTRACT ..................................................................","cbCaicFOHihh7Xi9","https://ap.wps.com/l/cbCaicFOHihh7Xi9","pdf",1872020,1,64,"English","en",105,"# ABSTRACT\n# LIST OF TABLES\n# LIST OF FIGURES\n# ABBREVIATIONS\n# 1. INTRODUCTION\n## 1.1 INTRODUCTION\n## 1.2 PROBLEM STATEMENT\n## 1.3 RESEARCH OBJECTIVES\n## 1.4 RESEARCH QUESTIONS\n## 1.5 SIGNIFICANCE OF THE STUDY\n## 1.6 REPORT ORGANIZATION\n# 2. BACKGROUND AND LITERATURE REVIEW\n## 2.1 INTRODUCTION\n## 2.2 BACKGROUND\n## 2.2.1 Overview of 5G Technology\n## 2.2.2 5G Advancements and Its Implications\n## 2.2.3 Understanding 5G Infrastructure\n## 2.2.4 MQTT in IoT","[{\"question\":\"What security problem does this thesis address in 5G IoT networks?\",\"answer\":\"It targets the need to strengthen security mechanisms for IoT systems operating over 5G networks, focusing on detecting and predicting intrusions affecting user security.\"},{\"question\":\"Why is MQTT central to the proposed approach?\",\"answer\":\"MQTT is highlighted as a widely adopted lightweight messaging protocol in IoT environments, and the study focuses on security analysis using MQTT-related data and behaviors.\"},{\"question\":\"Which dataset and machine learning method are used, and what performance is reported?\",\"answer\":\"The thesis uses the MQTT-IoT-IDS2020 dataset and employs a Catboost-based model, reporting an accuracy of 99.99%, exceeding previously established benchmarks.\"}]","IMPROVING 5G NETWORK SECURITY USING MACHINE LEARNING WITH MQTT DATA ANALYSIS - Master’s Thesis | PDF",1785736260,161,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improving-5g-network-security-using-machine-learning-with-mqtt-data-analysis-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-5g-network-security-using-machine-learning-with-mqtt-data-analysis-masters-thesis/121563/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What security problem does this thesis address in 5G IoT networks?","Question",{"text":75,"@type":76},"It targets the need to strengthen security mechanisms for IoT systems operating over 5G networks, focusing on detecting and predicting intrusions affecting user security.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is MQTT central to the proposed approach?",{"text":80,"@type":76},"MQTT is highlighted as a widely adopted lightweight messaging protocol in IoT environments, and the study focuses on security analysis using MQTT-related data and behaviors.",{"name":82,"@type":73,"acceptedAnswer":83},"Which dataset and machine learning method are used, and what performance is reported?",{"text":84,"@type":76},"The thesis uses the MQTT-IoT-IDS2020 dataset and employs a Catboost-based model, reporting an accuracy of 99.99%, exceeding previously established benchmarks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]