Article
DETECTING WEB ATTACKS WITH END-TO-END DEEP LEARNING
The increasing frequency and complexity of web attacks require strong security mechanisms to protect modern digital infrastructures. Traditional web attack detection systems mainly rely on predefined rules or signaturebased methods, which can often be bypassed by advanced and evolving malicious techniques. This paper proposes a deep learning-based approach for detecting web attacks using an end-to-end learning framework that improves the identification and prevention of web-based threats. The proposed system utilizes a Deep Neural Network (DNN) to analyze patterns and anomalies in web traffic data. Through end-to-end learning, the model automatically extracts meaningful features from raw input data, eliminating the need for manual feature engineering. This capability allows the system to adapt more effectively to new and previously unseen attack patterns. The model is designed to detect various types of web attacks, including SQL injection, cross-site scripting (XSS), and distributed denial-of-service (DDoS) attacks. The study also discusses important stages such as web traffic data collection, preprocessing, model training, and system optimization. Additionally, the detection model can be integrated with existing web security frameworks to enhance protection mechanisms. By utilizing advanced deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the system achieves high detection accuracy and supports real-time threat identification. This research highlights the potential of deep learning techniques in cybersecurity by providing an adaptive and proactive solution capable of evolving with emerging web attack strategies.
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