A Hybrid Security Framework for Web Applications Using Blockchain and Adaptive Adversarial Learning

Abstract

Web applications are increasingly vulnerable to sophisticated cyberattacks, and traditional security methods often fail to address the dynamic nature of modern threats. To tackle these challenges, we propose a novel security model that integrates blockchain technology, deep learning, and adaptive adversarial learning (ARL). This model aims to enhance web application security by ensuring data integrity, enabling intelligent attack detection, and optimizing defense strategies in real time. By combining these advanced technologies, our model offers a scalable and adaptive solution capable of defending against both known and unknown attacks. Experimental results demonstrate that our approach outperforms existing methods, providing superior protection and resilience against a wide range of cyber threats. Our model not only improves detection accuracy but also significantly enhances response times and overall defense efficiency. These results highlight the effectiveness of the proposed model in providing robust and efficient protection for web applications, offering significant improvements over traditional methods in handling dynamic and evolving cyber threats.