MR.V. SRINIVAS, S. ABHINAY, V. BHARGAV, U. SRINIVAS SAGAR, V. SIDDU, R. HARSHAVARDHAN 03 03 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 learningbased approach for detecting web attacks using an endtoend learning framework that improves the identification and prevention of webbased threats The proposed system utilizes a Deep Neural Network DNN to analyze patterns and anomalies in web traffic data Through endtoend 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 crosssite scripting XSS and distributed denialofservice 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 realtime 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 Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026