DETECTING WEB ATTACKS WITH END O END DEEP LEARNING

Abstract

In today’s digital era, web applications are increasingly targeted by sophisticated attacks such as SQL injection, cross-site scripting (XSS), and distributed denial-of-service (DDoS). Traditional security mechanisms, such as signature-based intrusion detection systems, struggle to detect novel or evolving attack patterns. This research proposes an end-to-end deep learning framework for detecting web attacks with high accuracy and minimal manual intervention. The system leverages raw HTTP request data, automatically learning intricate patterns and anomalies without the need for handcrafted features. By employing architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the framework can capture both spatial and temporal characteristics of web traffic. Experimental results demonstrate the model’s superior performance in identifying diverse attack types, achieving high precision and recall while maintaining low false positive rates. This approach provides a scalable, adaptive, and robust solution to modern web security challenges, bridging the gap between traditional methods and intelligent, automated detection systems.