End - to - End Deep Learning for Detecting Web Attacks

Web attacks, such as SQL injection, cross-site scripting (XSS), and distributed denial-ofservice (DDoS), pose significant threats to the security and integrity of online systems. Traditional detection methods, relying on rulebased systems or shallow machine learning, often struggle to keep pace with the evolving sophistication of these attacks. This paper proposes an end-to-end deep learning framework for detecting web attacks, leveraging the power of neural networks to automatically learn complex patterns and features from raw web traffic data. Unlike conventional approaches that require extensive manual feature engineering, our method processes unprocessed HTTP request logs and network traffic directly, enabling a fully automated detection pipeline. The proposed model employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both spatial and temporal dependencies in web traffic data. CNN layers extract local patterns, such as malicious payloads or anomalous headers, while RNN layers, enhanced with attention mechanisms, model sequential behaviors indicative of attacks. The framework is trained endto-end on a diverse dataset comprising benign traffic and real-world attack samples, ensuring robustness across various attack vectors. Preprocessing is minimized to tokenization and normalization, preserving the raw data's integrity and reducing human intervention. Evaluation results demonstrate that the model achieves superior performance compared to traditional methods, with an accuracy of over 95% and a low false-positive rate. It effectively identifies both known and zero-day attacks by generalizing learned representations. Additionally, the system scales efficiently, processing large volumes of traffic in real-time, making it suitable for deployment in production environments. This end-to-end deep learning approach not only enhances detection accuracy but also adapts to emerging threats, offering a proactive solution to the growing challenge of web security. Future work will focus on integrating explainability to improve trust and interpretability in security operations.