Securing Web Application using Web Application Firewall (WAF) and Machine Learning

Web application security has become essential for any business, particularly in light of the growing prevalence of web application attacks that bypass security defenses and the ongoing improvement of servers and software frameworks. The widespread adoption of web-based applications results from technological development and the digital revolution. Web applications are at high risk for security breaches because they were not created securely, have bugs, and are simple targets for hackers. New approaches and attack techniques are being used to access the web application without authorization to infiltrate the system, steal, and destroy the data. Defending the system from attacks like DDoS, SQL Injection, Cross-Site Scripting, etc., can be challenging. Web application firewalls (WAF) have been developed to protect against web application attacks. Web application firewalls use both anomaly-based and signature-based techniques. Data packets moving to and from a web application are monitored, filtered, and blocked by a web application firewall (WAF). In this paper, Natural language processing methods and a various machine learning algorithm were used to develop an anomaly-based web application firewall model. Our suggested approach achieved a classification accuracy of 99% using various payloads.