[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124410-en":3,"doc-seo-124410-105":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},124410,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Vulnerability Detection in Ethereum Smart Contracts via Machine Learning - A Qualitative Analysis","Smart contracts are fundamental to many blockchain applications, yet their widespread adoption is slowed by security vulnerabilities that can trigger substantial financial losses. This survey examines the state of machine-learning approaches for vulnerability detection in Ethereum smart contracts, organizing existing tools and methods, assessing their performance, and exposing key limitations. The critique highlights issues such as incomplete vulnerability coverage and dataset construction flaws, leading to new metrics for fair comparison. Findings support best practices to improve accuracy, scope, and efficiency, guiding future research and development.","arXiv :2407 . 18639v 1 [ cs .CR] 26 Jul 2024  \nVulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis  \nDALILA RESSI, Ca’ Foscari University, Italy ALVISE SPANÒ, Ca’ Foscari University, Italy  \nLORENZO BENETOLLO, University of Camerino, Italy and Ca’ Foscari University, Italy  \nCARLA PIAZZA, University of Udine, Italy MICHELE BUGLIESI, Ca’ Foscari University, Italy SABINA ROSSI, Ca’ Foscari University, Italy  \nSmart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole.  \nCCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Vulnera  \nbility management; • Computing methodologies → Machine learning.  \nAdditional Key Words and Phrases: Vulnerability Detection, Machine Learning, Ethereum Smart Contracts ACM Reference Format:  \nDalila Ressi, Alvise Spanò, Lorenzo Benetollo, Carla Piazza, Michele Bugliesi, and Sabina Rossi. 2024. Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis. 1, 1 (July 2024), 35 pages. [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 INTRODUCTION  \nBlockchain technology has gained widespread adoption as an effective infrastructure for developing decentralized applications. Among the many platforms currently available, Ethereum has emerged as (one of) the most popular, the first to introduce smart contracts as executable programs that enforce the rules specified by their code.  \nAuthors’ Contact Information: Dalila Ressi, [dalila.ressi@unive.it](dalila.ressi@unive.it), Ca’ Foscari University, Venice, Italy; Alvise Spanò, alvise. [spano@unive.it](spano@unive.it), Ca’ Foscari University, Venice, Italy; Lorenzo Benetollo, [lorenzo.benetollo@unive.it](lorenzo.benetollo@unive.it), University of Camerino, Camerino, Italy and Ca’ Foscari University, Venice, Italy; Carla Piazza, University of Udine, Udine, Italy, carla.piazza@uniud.it;  \nMichele Bugliesi, [bugliesi@unive.it](bugliesi@unive.it), Ca’ Foscari University, Venice, Italy; Sabina Rossi, [sabina.rossi@unive.it](sabina.rossi@unive.it), Ca’ Foscari  \nUniversity, Venice, Italy.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires [prior specific permissi","cbCaigNx4U0WdZqX","https://ap.wps.com/l/cbCaigNx4U0WdZqX","pdf",875046,1,35,"English","en",105,"# Introduction\n## Background and motivation\n## Ethereum smart contracts architecture and ecosystem\n## Security vulnerabilities and real-world impact\n# Survey scope and methodology\n## Categorization of ML-based detection tools\n## Evaluation and limitation analysis\n## Metrics and comparison challenges\n# Best practices and future directions","[{\"question\":\"What problem does the document address in Ethereum smart contract security?\",\"answer\":\"It addresses how security vulnerabilities in Ethereum smart contracts can be exploited, leading to significant financial losses and undermining trust in blockchain technology.\"},{\"question\":\"How does the survey evaluate machine-learning vulnerability detection for Ethereum smart contracts?\",\"answer\":\"It categorizes existing tools and methodologies, evaluates them, highlights limitations, and uses the critique to propose new metrics for more reliable comparison.\"},{\"question\":\"What key limitations are identified for current approaches and datasets?\",\"answer\":\"The document points to restricted vulnerability coverage and dataset construction flaws, which hinder sound comparisons and limit effectiveness.\"}]","Vulnerability Detection in Ethereum Smart Contracts via Machine Learning - A Qualitative Analysis | PDF",1785822068,88,{"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},"vulnerability-detection-in-ethereum-smart-contracts-via-machine-learning-a-qualitative-analysis","",{"@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/vulnerability-detection-in-ethereum-smart-contracts-via-machine-learning-a-qualitative-analysis/124410/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address in Ethereum smart contract security?","Question",{"text":75,"@type":76},"It addresses how security vulnerabilities in Ethereum smart contracts can be exploited, leading to significant financial losses and undermining trust in blockchain technology.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the survey evaluate machine-learning vulnerability detection for Ethereum smart contracts?",{"text":80,"@type":76},"It categorizes existing tools and methodologies, evaluates them, highlights limitations, and uses the critique to propose new metrics for more reliable comparison.",{"name":82,"@type":73,"acceptedAnswer":83},"What key limitations are identified for current approaches and datasets?",{"text":84,"@type":76},"The document points to restricted vulnerability coverage and dataset construction flaws, which hinder sound comparisons and limit effectiveness.","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"]