[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121344-en":3,"doc-seo-121344-105":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},121344,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Systematic Literature Review - SQL Injection Detection Vulnerability Using Machine Learning","SQL Injection (SQLI) is a database security attack that leverages weaknesses caused by improperly monitored user input in web applications, making it a critical part of information system security. Ongoing research explores more effective detection and prevention approaches, including Machine Learning algorithms such as Random Forest, Naïve Bayes, Support Vector Machine, neural network variants, and others. This systematic literature review compares algorithm performance for detecting SQLI vulnerabilities using related evaluation metrics. Findings from the literature indicate that Random Forest and SVM provide superior performance values.","Systematic Literature Review: SQL Injection Detection Vulnerability Using Machine Learning  \n1Agnes Rahayu, 2Eva Yulyanti, 3Muhammad Ghalib  \n1,2,3 Program Studi Magister Ilmu Komputer Universitas Budi Luhur  \nJl. Ciledug Raya No.99, Petukangan Utara, Kec. Pesanggrahan, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta 12260  \nE-mail: [rahayuagnes.ar@gmail.com](rahayuagnes.ar@gmail.com1)[1](rahayuagnes.ar@gmail.com1), [evayulyanti02@gmail.com](evayulyanti02@gmail.com2)[2](evayulyanti02@gmail.com2), [muhammadghalibb18@gmail.com](muhammadghalibb18@gmail.com3)[3](muhammadghalibb18@gmail.com3)  \n(Received: Nopember 2024, Revised: Februari 2025, Accepied: April 2025)  \nAbstract — SQL Injection (SQLI) is a security attack on databases that exploits loopholes or vulnerabilities in improperly monitored user input in web applications and is an important aspect of information system security. Research and development continues to be carried out using more effective methods to detect and prevent SQLI, including the use of Machine Learning algorithms such as Random Forest, Naïve Bayes, Support Vector Machine, Neutral Network, Knearest, Decision Tree and others. The focus of this research is to compare the performance results of each algorithm. This research compares the performance of each algorithm in detecting SQLI vulnerabilities against a set of related metrics. The results of the analysis based on the literature study show that Random Forest and Support Vector Machine (SVM) have superior value.  \nKeywords: SQL Injection, injection vulnerability detection, Machine Learning, Support Vector Machine, Random Forest.  \nAbstrak—SQL Injection (SQLI) adalah serangan keamananpada database yang mengeksploitasi celah atau kerentananpada input pengguna yang tidak dipantau dengan benar dalamaplikasi web dan merupakan aspek penting dalam keamanansistem informasi. Penelitian dan pengembangan terus dilakukan menggunakan metode yang lebih efektif untuk mendeteksi dan mencegah SQLI, termasuk penggunaan algoritma Machine Learning seperti Random Forest, Naïve Bayes, Support Vector Machine, Neutral Network, Knearest, Decision Tree dan lainnya. Fokus penelitian ini adalah melakukan perbandinganterhadap hasil kinerja dari masing-masing algoritma. Penelitian ini membandingkan kinerja setiap algoritma dalam mendeteksikerentanan SQLI terhadap serangkaian metrik terkait. Hasil analisis berdasarkan studi literatur menunjukan bahwa Random Forest dan Support Vector Machine (SVM) memiliki nilai kinerjayang unggul.  \nKata Kunci: SQL Injection, injection vulnerability detection, Machine Learning, Support Vector Machine, Random Forest.  \nI. PENDAHULUAN  \nKeamanan aplikasi web adalah aspek penting untuk memastikan integritas dan keberlanjutan sebuah sistem informasi. Salah satu ancaman paling serius terhadapkeamanan aplikasi web adalah SQL Injection (SQLI). SQL Injection adalah sebuah aksi hacking yang dilakukan diaplikasi client dengan cara memodifikasi perintah SQL yang ada di memori aplikasi client, SQL Injection merupakan teknik eksploitasi aplikasi berbasis yang didalamnya menggunakan basis data untuk penyimpanandata [1]  \nDalam beberapa tahun terakhir, pendekatan berbasis Machine Learning (ML) telah muncul sebagai alternatif yang menjanjikan untuk mendeteksi dan memitigasiserangan SQLI. Dengan menggunakan teknik ML, sistem keamanan dapat belajar mengenali pola mencurigakandalam Query SQL, sehingga memungkinkan sistem mendeteksi dan mencegah serangan sebelummenyebabkan kerusakan.  \nPenelitian sebelumnya telah menyelidiki berbagaimetode Machine Learning untuk mendeteksi SQLI, termasuk Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Neutral Network, dan Support Vector Machine (SVM). Eksperimen dalam literatur telah mampu membandingkan kinerja masing-masing metode ini dalam mendeteksi serangan SQLI menggunakan kumpulan data terkait yang berisi sampel Query SQL yang aman dan berpotensi berbahaya.  \nTinjauan literatur menyajikan hasil penelitian sebelumnya yan","cbCaiiJzTKkg3Vau","https://ap.wps.com/l/cbCaiiJzTKkg3Vau","pdf",503014,1,6,"English","en",105,"# I. PENDAHULUAN\n# II. TINJAUAN PUSTAKA\n## Research Questions (PICOC)\n## Population, Intervention, Comparison, Outcomes, Context","[{\"question\":\"What is the main security threat discussed in the review?\",\"answer\":\"The review focuses on SQL Injection (SQLI), a database attack that exploits improperly monitored user input in web applications.\"},{\"question\":\"Which Machine Learning algorithms are compared for detecting SQLI vulnerabilities?\",\"answer\":\"The document discusses comparing several ML algorithms, including Random Forest, Naïve Bayes, K-Nearest Neighbors, neural network variants, and Support Vector Machine (SVM).\"},{\"question\":\"What criteria does the study use to frame the research questions?\",\"answer\":\"The research questions are structured using the PICOC framework, covering Population, Intervention, Comparison, Outcomes, and Context.\"}]","Systematic Literature Review - SQL Injection Detection Vulnerability Using Machine Learning | PDF",1785735163,15,{"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},"systematic-literature-review-sql-injection-detection-vulnerability-using-machine-learning","",{"@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/systematic-literature-review-sql-injection-detection-vulnerability-using-machine-learning/121344/",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 security threat discussed in the review?","Question",{"text":75,"@type":76},"The review focuses on SQL Injection (SQLI), a database attack that exploits improperly monitored user input in web applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which Machine Learning algorithms are compared for detecting SQLI vulnerabilities?",{"text":80,"@type":76},"The document discusses comparing several ML algorithms, including Random Forest, Naïve Bayes, K-Nearest Neighbors, neural network variants, and Support Vector Machine (SVM).",{"name":82,"@type":73,"acceptedAnswer":83},"What criteria does the study use to frame the research questions?",{"text":84,"@type":76},"The research questions are structured using the PICOC framework, covering Population, Intervention, Comparison, Outcomes, and Context.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"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":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"]