ENHANCING WEB DEFENSE THROUGH MACHINE LEARNING AND ACTIVE RESPONSE MECHANISM INTEGRATION IN WAF

Today, web applications play an important role in modern digital infrastructure by providing users with public access to services and information quickly and flexibly. Web application security is critical due to the increasing complexity of cyber attacks. This study proposes a new working concept that combines machine learning and active response mechanisms in Web Application Firewalls (WAFs) with the support of the concept of creating libraries that make WAFs more adaptive.
The current state of web application security is defined by the weaknesses of conventional WAFs, which often struggle to withstand changing cyber threats such as zero-day attacks and new attack anomalies due to the static rules and signature bases used in WAFs. On the other hand, dynamic cyber threats are always evolving and can evade conventional WAFs. To stay up to date with the latest cyber dangers, WAF signatures and rules must be updated regularly,
however, this can be a difficult task, time consuming and lacks awareness on the part of various parties.
Using techniques that incorporate machine learning, this paper proposes a WAF concept for identifying and categorizing malicious activities. To ensure robustness and flexibility, the model is trained on a variety of datasets covering various attack scenarios. Developing a library that can also generate future attack patterns is an important step in active response system integration. This concept is intended to better anticipate current and future potential threats or what is known as a zero-day attack. This paper's approach combines supervised and unsupervised learning approaches for initial training and ongoing learning to respond to new threats.

Keyword: Web Application Security, Machine Learning, Web Application Firewall, WAF, Dynamic Security Framework, Zero-day