Proactive Cyber Security: End-To-End Deep Learning For Web Attack Mitigation

Abstract

As web attacks grow increasingly sophisticated, the need for advanced and proactive security solutions has never been greater. Traditional rule-based and signature-based systems struggle to keep pace with evolving threats, necessitating innovative approaches. This paper proposes an end-to-end deep learning-based framework for detecting and mitigating web attacks in real-time. By leveraging deep neural networks, the system analyzes web traffic to identify malicious patterns, such as SQL injection, cross-site scripting (XSS), and denial-of-service (DoS) attacks. The system's ability to learn from vast amounts of data enables it to adapt to new and unknown threats, significantly reducing false positives and improving detection accuracy. The proposed solution offers a scalable, adaptive, and automated defense mechanism, ensuring robust protection for web applications against a wide range of cyber threats.