Systematic Literature Review: SQL Injection Detection Vulnerability Using Machine Learning
Abstract
SQL Injection (SQLI) adalah serangan keamanan pada database yang mengeksploitasi celah atau kerentanan pada input pengguna yang tidak dipantau dengan benar dalam aplikasi web dan merupakan aspek penting dalam keamanan sistem 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 perbandingan terhadap hasil kinerja dari masing-masing algoritma. Penelitian ini membandingkan kinerja setiap algoritma dalam mendeteksi kerentanan SQLI terhadap serangkaian metrik terkait. Hasil analisis berdasarkan studi literatur menunjukan bahwa Random Forest dan Support Vector Machine (SVM) memiliki nilai kinerja yang unggul.
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