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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
DAA Pertiwi
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Computer Science & IT

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Perbandingan Akurasi Machine Learning dan Deep Learning dalam Deteksi Serangan SQL Injection Franki SW; Jumanto Unjung; DAA Pertiwi; Much. Aziz Muslim
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

SQL Injection (SQLi) attacks are among the most common threats to web application security, potentially leading to data breaches and unauthorized manipulation of database systems. The limitations of traditional detection mechanisms, such as Web Application Firewalls (WAF), highlight the need for intelligent approaches capable of adapting to emerging attack patterns. This study aims to develop an effective, accurate, and adaptive SQL Injection detection model by comparing the performance of the Random Forest algorithm as a representation of traditional Machine Learning and the Multilayer Perceptron (MLP) as a representation of Deep Learning. The evaluation focuses on classification accuracy, processing speed, and implementation simplicity using an identical SQL Injection attack dataset. The results of this study are expected to provide recommendations for an optimal detection model to enhance web application security and strengthen defense systems against code injection-based cyber threats.
Co-Authors Franki SW Jumanto Unjung Much. Aziz Muslim