<![CDATA[This research is motivated by the increasing need for web security systems along with the rapid development of web-based applications and the rise of cyber threats such as Cross-Site Scripting (XSS), SQL Injection (SQLi), and HTTP manipulation. This study aims to review the application of Deep Learning methods in web attack detection with a focus on frequently used algorithms, model performance, and challenges faced. This study uses a systematic literature study approach by analyzing various relevant scientific publications. Data were collected from reliable literature sources and analyzed qualitatively to identify patterns, methods, and research gaps. The results of the study indicate that Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) algorithms are the most widely used methods and have good performance in detecting web attacks. However, several obstacles remain such as limited representative datasets, data imbalance, and high computational requirements. This study contributes in the form of a comprehensive synthesis of the application of Deep Learning in web security, as well as identifying research gaps and development opportunities to improve model effectiveness and efficiency, particularly in the implementation of real-time attack detection.]]>
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