[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121384-en":3,"doc-seo-121384-105":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},121384,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Ransomware Classification on Blockchain Using Machine Learning - Thesis","The thesis investigates ransomware classification in a blockchain context using machine learning to address the security risks posed by malicious activity. It evaluates and compares multiple approaches, including Random Forest, Convolutional Neural Network (CNN), and ensemble methods, to determine which techniques detect fraudulent behavior most effectively. The study applies dataset filtering and class-imbalance handling via under-sampling and over-sampling, then measures performance with precision, recall, and F1-score to support model selection.","CALIFORNIA STATE UNIVERSITY SAN MARCOS  \nTHESIS SIGNATURE PAGE  \nTHESIS SUBMITTED IN PARTIAL FULFILLMENT  \nOF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nIN  \nCOMPUTER SCIENCE  \nTHESIS TITLE: RANSOMWARE CLASSIFICATION ON BLOCK CHAIN USING MACHINE LEARNING.  \nAUTHOR: Pritsam Dabre  \nDATE OF SUCCESSFUL DEFENSE: 4/29/2024  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN  \nPARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE.  \nNahid Ebrahimi Majid  \nTHESIS COMMITTEE CHAIR  \nDr. Yanayan Li  \nTHESIS COMMITTEE MEMBER  \nSIGNATURE  \nDATE  \nReport  \nReport .......................................................................................................................................... 2  \nCHAPTER 1: INTRODUCTION .............................................................................................. 3  \n1.1 Problem Statement ........................................................................................................... 3  \n1.2 Purpose of the Study and Motivation ............................................................................. 3  \n1.3 Research Methodology .................................................................................................... 3  \n1.4 Research Scope ................................................................................................................ 4  \nCHAPTER 2: RELATED WORK............................................................................................. 4  \n2.1 Limitations and Challenges ............................................................................................. 5  \n2.2 Gaps and Opportunities for Further Research ................................................................ 6  \nCHAPTER 3: METHODOLOGY ............................................................................................. 6  \n3.1 Dataset .............................................................................................................................. 6  \n3.2 Data Pre-processing ......................................................................................................... 7  \nUnder sampling and oversampling........................................................................................ 7  \n3.3 Proposed Approach .......................................................................................................... 9  \nCHAPTER 4: ANALYSIS OF THE RESULTS .................................................................... 10  \n4.1 Evaluation Metrics ......................................................................................................... 10  \n4.3 Discussion....................................................................................................................... 17  \nCHAPTER 5: CONCLUSION................................................................................................. 18  \nREFERENCES.......................................................................................................................... 20  \nCHAPTER 1: INTRODUCTION  \n1.1 Problem Statement  \nWith the increasing popularity of Bitcoin transactions, there has been a corresponding surge in fraudulent activities within the cryptocurrency ecosystem. Fraudulent transactions not only compromise the integrity of the Bitcoin network but also pose significant financial risks to users. Traditional rule-based approaches to fraud detection often fall short in effectively identifying and mitigating these fraudulent activities. Therefore, there is a pressing need for robust machine learning-based solutions capable of detecting and preventing fraudulent Bitcoin transactions.  \n1.2 Purpose of the Study and Motivation  \nThe primary purpose of this study is to explore and compare different machine learning approaches for detecting fraudulent Bitcoin transactions. By evaluating the performance of Random Forest, Convolutional Neural Network (CNN), and ensemble methods, we aim ","cbCaik9jBDTxpoep","https://ap.wps.com/l/cbCaik9jBDTxpoep","pdf",995489,1,21,"English","en",105,"# Chapter 1: Introduction\n## 1.1 Problem Statement\n## 1.2 Purpose of the Study and Motivation\n## 1.3 Research Methodology\n## 1.4 Research Scope\n# Chapter 2: Related Work\n## 2.1 Limitations and Challenges\n## 2.2 Gaps and Opportunities for Further Research\n# Chapter 3: Methodology\n## 3.1 Dataset\n## 3.2 Data Pre-processing\n## 3.3 Proposed Approach\n# Chapter 4: Analysis of the Results\n## 4.1 Evaluation Metrics\n## 4.3 Discussion\n# Chapter 5: Conclusion\n# References","[{\"question\":\"What is the main problem the thesis addresses?\",\"answer\":\"The study targets the detection and prevention of malicious activities affecting blockchain-related transactions, emphasizing the limitations of traditional rule-based fraud detection.\"},{\"question\":\"Which machine learning models are compared for classification?\",\"answer\":\"The thesis compares Random Forest, CNN, and ensemble methods, including combinations such as AdaBoost and Gradient Boosting.\"},{\"question\":\"How does the thesis handle class imbalance in the dataset?\",\"answer\":\"It uses under-sampling and over-sampling techniques during data preprocessing to balance classes associated with fraudulent activity.\"}]","Ransomware Classification on Blockchain Using Machine Learning - Thesis | PDF",1785735397,53,{"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},"ransomware-classification-on-blockchain-using-machine-learning-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/ransomware-classification-on-blockchain-using-machine-learning-thesis/121384/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main problem the thesis addresses?","Question",{"text":75,"@type":76},"The study targets the detection and prevention of malicious activities affecting blockchain-related transactions, emphasizing the limitations of traditional rule-based fraud detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared for classification?",{"text":80,"@type":76},"The thesis compares Random Forest, CNN, and ensemble methods, including combinations such as AdaBoost and Gradient Boosting.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis handle class imbalance in the dataset?",{"text":84,"@type":76},"It uses under-sampling and over-sampling techniques during data preprocessing to balance classes associated with fraudulent activity.","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"]