[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123708-en":3,"doc-seo-123708-105":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},123708,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches","Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches focuses on detecting fraudulent transactions within the Ethereum blockchain to reduce financial losses and strengthen platform security. The study uses a public dataset of 9,841 Ethereum transactions, extracting transaction attributes such as gas price, transaction fee, and timestamp. It applies data preprocessing followed by predictive modeling with multiple ML methods, including decision trees, logistic regression, gradient boosting, and XGBoost, and introduces a hybrid random-forest and deep neural network model achieving 97.16% precision.","Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning  \nApproaches  \nSwapna Siddamsetti1, Dr Muktevi Srivenkatesh2  \n1Department of Computer Science, GITAM School of Science, GITAM Deemed to be University, Vishakapatnam, and Assistant Professor, Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Hyderabad, Telangana, India.  \n[swapnangit2021@gmail.com](swapnangit2021@gmail.com)  \n2Associate Professor, Department of Computer Science, GITAM School of Science, GITAM Deemed to be University, Vishakapatnam, India.  \n[srivenkatesh.muktevi@gitam.edu](srivenkatesh.muktevi@gitam.edu)  \nAbstract—Background: This paper tackles the critical challenge of detecting fraudulent transactions within the Ethereum blockchain using machine learning techniques. With the burgeoning importance of blockchain, ensuring its security against fraudulent activities is crucial to prevent significant monetary losses. We utilized a public dataset comprising 9,841 Ethereum transactions, characterized by attributes such as gas price, transaction fee, and timestamp.Methods: Our approach is bifurcated into two core phases: data preprocessing and predictive modeling. In the data preprocessing phase, we meticulously process the dataset and extract pivotal features from transactions, setting the stage for efficient predictive modeling.Findings: For predictive modeling, we employed several machine learning algorithms to discern between fraudulent and legitimate transactions. Our evaluation encompassed algorithms like decision trees, logistic regression, gradient boosting, XGBoost, and an innovative hybrid model that melds random forests with deep neural networks (DNN).Novelty: Our findings underscore that the proposed model boasts a precision rate of 97.16%, marking a substantial leap in fraudulent transaction detection on the Ethereum blockchain in comparison to prevailing methodologies. This paper augments the current efforts aimed at bolstering the security of blockchain transactions using sophisticated analytical strategies..  \nKeywords-Machine Learning algorithms, Blockchain, Deep learning, Fraud Detection, Ethereum.  \nI. INTRODUCTION  \nThe decentralized nature of the blockchain concept, which operates as a publicly accessible ledger, has garnered significant interest from various industries and scholars. The concept of blockchain was initially presented in a scholarly document by Nakamoto in 2008 [1] . A blockchain is an electronic ledger that operates decentralized and enables the recording, propagation, and synchronization of transactions across multiple ledgers. The decentralized structure of blockchain technology allows transactions to be executed without intermediaries, rendering it a suitable option for various financial services such as online payments, remittances, and digital assets [2; 3] . Notwithstanding its potential benefits, blockchain technology has demonstrated susceptibility to security risks and assaults, as exemplified by previous incursions on cryptocurrencies that rely on blockchain [4] . The Ethereum platform, which operates on adecentralized blockchain network and is renowned for its ability to execute smart contracts, has experienced two separate attacks resulting in considerable disruptions to its network operations [5] . The assaults mentioned above were carried out without the express permission of the platform  \nadministrators. The decentralized architecture of Ethereum enables individuals to participate in digital transactions with minimal transaction costs and robust security measures. The extensive user base of the platform catalyzes for developers to introduce their network applications, thereby consolidating Ethereum's position as a powerful platform for decentralized applications, encompassing DeFi and NFTs.  \nWith automation converging with blockchain, its adoption has surged in sectors like online finance, IoT, healthcare, and more [6,7] . Ethereum stands","cbCaigyUy0VBLp3Z","https://ap.wps.com/l/cbCaigyUy0VBLp3Z","pdf",460083,1,12,"English","en",105,"# Introduction\n## Background on blockchain and Ethereum\n## Motivation for fraud detection\n# Methods\n## Data preprocessing and feature extraction\n## Predictive modeling with ML and hybrid deep learning\n# Results\n## Evaluation of algorithms and performance\n# Conclusion\n## Impact on blockchain transaction security","[{\"question\":\"What problem does the paper address in the Ethereum blockchain?\",\"answer\":\"It targets the detection of fraudulent transactions within Ethereum to prevent significant monetary losses and improve security.\"},{\"question\":\"What dataset and features are used for the proposed approach?\",\"answer\":\"The study uses a public dataset containing 9,841 Ethereum transactions and extracts attributes such as gas price, transaction fee, and timestamp.\"},{\"question\":\"Which models are used for fraud vs. legitimate transaction classification?\",\"answer\":\"Several algorithms are tested, including decision trees, logistic regression, gradient boosting, XGBoost, and a hybrid model combining random forests with deep neural networks.\"}]","Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches | PDF",1785818123,30,{"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},"efficient-fraud-detection-in-ethereum-blockchain-through-machine-learning-and-deep-learning-approaches","",{"@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/efficient-fraud-detection-in-ethereum-blockchain-through-machine-learning-and-deep-learning-approaches/123708/",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","2026-08-04",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 problem does the paper address in the Ethereum blockchain?","Question",{"text":75,"@type":76},"It targets the detection of fraudulent transactions within Ethereum to prevent significant monetary losses and improve security.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and features are used for the proposed approach?",{"text":80,"@type":76},"The study uses a public dataset containing 9,841 Ethereum transactions and extracts attributes such as gas price, transaction fee, and timestamp.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are used for fraud vs. legitimate transaction classification?",{"text":84,"@type":76},"Several algorithms are tested, including decision trees, logistic regression, gradient boosting, XGBoost, and a hybrid model combining random forests with deep neural networks.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]