[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121199-en":3,"doc-seo-121199-105":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},121199,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Analyzing Comparison Performance Model Of Machine Learning Through Detection Sql Injection Attack - Article","This research compares machine learning models designed to detect SQL Injection attacks within security systems. A dataset is collected from Kaggle’s highest-upvoted resource in the SQL Injection category. Three models are developed: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The workflow splits data into 70% training and 30% testing, evaluates effectiveness, and supports preventive measures. Results show SVM achieves 99.82% accuracy, 99.88% precision, and 99.34% recall, while KNN and LR provide lower but strong performance, improving overall system security.","JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal homepage: [https://jurnal.stkippgritulungagung.ac.id/index.php/jipi](https://jurnal.stkippgritulungagung.ac.id/index.php/jipi)  \nISSN: 2540-8984  \nVol. 9, No. 4, Desember 2024, Pp. 2064-2073  \nANALYZING COMPARISON PERFORMANCE MODEL OF MACHINE LEARNING THROUGH DETECTION SQL INJECTION ATTACK  \nRakha Satria Pratama*1), Muhamad Irsan2), Rio Guntur Utomo3)  \n1. Information Technology, Informatics, Telkom University, Bandung  \n2. Information Technology, Informatics, Telkom University, Bandung  \n3. Information Technology, Informatics, Telkom University, Bandung  \nArticle Info  \nKeywords: SQL Injection, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Machine Learning  \nArticle history:  \nReceived 11 September 2024  \nRevised 5 Oktober 2024  \nAccepted 14 November 2024  \nAvailable online 4 December 2024  \nDOI :  \n[https://doi.org/10.29100/jipi.v9i4.781](https://doi.org/10.29100/jipi.v9i4.781)  \n* Corresponding author. Corresponding Author E-mail address:  \n[rakhasatria@student.telkomuniversity.ac.id](rakhasatria@student.telkomuniversity.ac.id)  \nABSTRACT  \nThis research aims to compare Machine Learning models that effectively detect SQL Injection attacks in security systems. The dataset was collected from the Kaggle resource published by Syed Saqlain Hussain Shah, the dataset with the highest upvotes in the SQL Injection category. The models developed include Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR) . The research process includes separating the data into 70% training and 30% test data, model training, testing model effectiveness, and implementing preventive measures against SQL Injection attacks. The research results show that the SVM model has an accuracy rate of 99.82%, precision of 99.88%, and recall (Sensitivity) of 99.34%. KNN obtained an accuracy rate of 79.28%, a precision of 98.38%, and a recall (Sensitivity) of 73.31% . LR obtained an accuracy rate of 98.99%, precision of 99.94%, and recall (Sensitivity) of 98.70% . Using a Machine Learning approach, this research improves system security against SQL Injection attacks.  \n.  \nI. INTRODUCTION  \nIData security has become the main focus of technological progress in the ever-growing digital era. One of the  \nmain challenges in a security context is combating increasingly sophisticated SQL Injection attacks, which can  \ninfiltrate systems in various ways [1] . SQL Injection attacks exploit security vulnerabilities by injecting malicious SQL code. The attack's impact was severe; attackers could easily access sensitive data in the database, manipulate, or even delete stored data [2] . This not only compromises the confidentiality of information but also the integrity and availability of data. Therefore, protection against SQL Injection attacks is essential in today's digital ecosystem.  \nAs a concrete example, an attack exploiting a zero-day vulnerability in Progress Software's MoveIT Transfer product has highlighted the threat that SQL Injection vulnerabilities pose to organizations ofall sizes. On May 31, 2023, Progress disclosed a critical SQL Injection vulnerability, tracked as CVE-2023-34362, which could allow an attacker to access an instance of MoveIT Transfer. A patch was released the same day, but security vendors soon reported widespread exploitation that began before the disclosure date. Microsoft later attributed the attack to a threat actor associated with the Clop ransomware group that it called “Lace Tempest.” Some of the victims of the data breach include HR software provider Zellis and the government of Nova Scotia, Canada [3] .  \nSQL Injection attacks have been identified as one of the highest-risk threats by the Open Web Application Security Project [4] . Incidents related to SQL Injection are becoming more frequent, emphasizing the need for careful analysis to detect and prevent such attacks [5] . One of the main approaches in this analys","cbCaivjLOpLXMxcF","https://ap.wps.com/l/cbCaivjLOpLXMxcF","pdf",368950,1,10,"English","en",105,"# Introduction\n## SQL Injection threat and impact\n## Motivation for machine learning-based detection\n## Overview of machine learning concepts","[{\"question\":\"Which machine learning models are compared for detecting SQL Injection attacks?\",\"answer\":\"The study compares Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR).\"},{\"question\":\"How is the dataset and model evaluation performed?\",\"answer\":\"The dataset is split into 70% training data and 30% test data. Models are trained, then tested to measure effectiveness before drawing conclusions.\"},{\"question\":\"What performance results does the research report for each model?\",\"answer\":\"SVM reaches 99.82% accuracy, 99.88% precision, and 99.34% recall. KNN achieves 79.28% accuracy, 98.38% precision, and 73.31% recall, while LR achieves 98.99% accuracy, 99.94% precision, and 98.70% recall.\"}]","Analyzing Comparison Performance Model Of Machine Learning Through Detection Sql Injection Attack - Article | PDF",1785734321,25,{"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},"analyzing-comparison-performance-model-of-machine-learning-through-detection-sql-injection-attack-article","",{"@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/analyzing-comparison-performance-model-of-machine-learning-through-detection-sql-injection-attack-article/121199/",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},"Which machine learning models are compared for detecting SQL Injection attacks?","Question",{"text":75,"@type":76},"The study compares Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset and model evaluation performed?",{"text":80,"@type":76},"The dataset is split into 70% training data and 30% test data. Models are trained, then tested to measure effectiveness before drawing conclusions.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results does the research report for each model?",{"text":84,"@type":76},"SVM reaches 99.82% accuracy, 99.88% precision, and 99.34% recall. 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