<![CDATA[Web applications are vital in today's digital infrastructure, offering fast and flexible access to services and information. However, they face security challenges due to increasingly sophisticated cyber attacks. This study addresses the problem of conventional Web Application Firewalls (WAFs) struggling to adapt to evolving threats like zero-day attacks because of their reliance on static rules and ssignatures. We propose a novel approach that integrates machine learning and active response mechanisms to enhance WAF adaptability. The methodology involves training a machine learning model on diverse datasets to identify and categorize malicious activities, ensuring robustness against various attack scenarios. Additionally, we develop a library to predict future attack patterns, facilitating an active response system. The results demonstrate improved detection and response to current and future threats by combining supervised and unsupervised learning for both initial training and ongoing adaptation, effectively enhancing WAF capabilities.]]>
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