[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119035-en":3,"doc-seo-119035-105":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},119035,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning approaches for enhancing smart contracts security - A systematic literature review","Smart contracts automate decentralized application workflows, yet their code immutability makes security vulnerabilities highly consequential, often leading to financial losses. Automatic vulnerability detection is therefore essential, especially for large blockchain codebases where manual auditing is inefficient. Machine learning (ML) has become a prominent approach, but a lack of systematic reviews makes it hard to identify research gaps. This systematic literature review analyzes 46 studies from multiple databases (2018–2023), addressing vulnerability identification, ML model approaches, and dataset sources. It also reviews drawbacks and highlights future research directions to improve ML-based security solutions.","Contents lists available at GrowingScience  \nInternational Journal of Data and Network Science  \n[homepage: www.GrowingScience.com/ijds](homepage: www.GrowingScience.com/ijds)  \nMachine learning approaches for enhancing smart contracts security: A systematic literature review  \nAreej AlShormana*, Fatima Shannaqb and Mohammad Shehabb  \naDepartment of Computer Science, Faculty of Prince Al-Hussein Bin Abdallah IIfor IT, Al al-Bayt University, Mafraq, Jordan  \nbCollege of Computer and Informatics, Amman Arab University, Amman, Jordan   \nC H R O N I C L E  \n\n| Article history:\u003Cbr>Received: January 3, 2024\u003Cbr>Received in revised format: February 29, 2024\u003Cbr>Accepted: April 12, 2024\u003Cbr>Available online: April 12, 2024 |\n| --- |\n| Keywords: Ethereum\u003Cbr>Smart Contracts Machine Learning Vulnerability Attack\u003Cbr>Detection |\n\nA B S T R A C T  \nSmart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.  \n© 2024 by the authors; licensee Growing Science, Canada.  \n1. Introduction  \nSmart contracts are being widely adopted across sectors like finance, supply chain, healthcare, etc. (Jiang et al., 2023; Shormanet al., 2020; Litke et al., 2019; de la Rosa et al., 2016; Garg et al., 2019). However, their immutability poses security concerns if vulnerabilities exist in code. As networks cannot be changed post-deployment, reviewing and testing contracts pre-deployment is critical. Attacks like the $150 million DAO hack highlight the need for improved security methods and best practices to prevent such incidents (Ivanov et al., 2023). Generally, the development of savvy contract applications requires satisfactory arrangements to identify bugs that were recently sent and distributed on blockchain systems. Formal confirmation distinguishes vulnerabilities in shrewd contracts, sometime recently arranged by characterizing determinations, making models, and utilizing instruments to analyze models. Be that as it may, formal confirmation is challenging, time-consuming, and requires noteworthy manual exertion. In differentiation, machine learning strategies can consequently identify irregularities and vulnerabilities, lessening manual exertion. Also, machine learning models are more adaptable as the number of keen contracts develops, whereas formal confirmation can end up computationally costly. Generally, machine learning complements formal confirmation by empowering cost-effective and adaptable defenselessness locations (Eshghie et al., 2021) . Machine learning for recognizing shrewd contract vulnerabilities has risen as an imperative investigation region as of late. Whereas numerous consider utilizing ML for defenselessness locations, a comprehensive survey of solidifying approaches is missing. This paper conducts an efficient overview of 46 papers from 2018-2023 to think about, analyze, and classify ML strategies for defenselessness discovery. The overview serves to develop an understanding of state-of-the-art lo","cbCaimitsys2Znk0","https://ap.wps.com/l/cbCaimitsys2Znk0","pdf",916667,1,20,"English","en",105,"# Introduction\n## Smart contract security challenges and need for automated detection\n## Role of machine learning vs formal verification\n# Related work and survey scope\n## Overview of existing surveys and systematic reviews\n# Research methodology\n## Paper selection criteria\n# Research questions and results\n## Vulnerability identification\n## Machine learning model approaches\n## Data sources and datasets\n# Limitations and conclusion","[{\"question\":\"Why is vulnerability detection critical for smart contracts before deployment?\",\"answer\":\"Smart contracts are immutable after deployment, so any vulnerabilities in the code can be exploited. Reviewing and testing beforehand reduces security risk and financial losses.\"},{\"question\":\"What does the systematic literature review analyze and how many studies are included?\",\"answer\":\"The review analyzes 46 papers published from 2018 to 2023, selecting studies using specified keywords and reviewing ML-driven smart contract vulnerability detection approaches.\"},{\"question\":\"Which aspects are covered by the review’s research questions?\",\"answer\":\"The review addresses three questions: how vulnerabilities are identified, what machine learning model approaches are used, and what data sources/datasets support the detection methods.\"}]","Machine learning approaches for enhancing smart contracts security - A systematic literature review | PDF",1785722027,50,{"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},"machine-learning-approaches-for-enhancing-smart-contracts-security-a-systematic-literature-review","",{"@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/machine-learning-approaches-for-enhancing-smart-contracts-security-a-systematic-literature-review/119035/",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},"Why is vulnerability detection critical for smart contracts before deployment?","Question",{"text":75,"@type":76},"Smart contracts are immutable after deployment, so any vulnerabilities in the code can be exploited. Reviewing and testing beforehand reduces security risk and financial losses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the systematic literature review analyze and how many studies are included?",{"text":80,"@type":76},"The review analyzes 46 papers published from 2018 to 2023, selecting studies using specified keywords and reviewing ML-driven smart contract vulnerability detection approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"Which aspects are covered by the review’s research questions?",{"text":84,"@type":76},"The review addresses three questions: how vulnerabilities are identified, what machine learning model approaches are used, and what data sources/datasets support the detection methods.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]