MACHINE LEARNING-BASED SOLUTIONS FOR PROACTIVE WEB VULNERABILITY ASSESSMENT

Abstract

Web applications are increasingly targeted by cyber attackers exploiting vulnerabilities to compromise security, privacy, and data integrity. Traditional vulnerability detection techniques often fall short in terms of accuracy, speed, and adaptability to evolving threats. This paper explores machine learning-based solutions to enhance proactive web vulnerability assessment. By analyzing historical vulnerability data, source code patterns, and web traffic features, machine learning models can predict and detect vulnerabilities more effectively. The study reviews various supervised and unsupervised learning algorithms applied in this domain, evaluates their performance, and discusses the challenges and future prospects. Results indicate that integrating machine learning significantly improves detection rates and reduces false positives, thus enabling timely mitigation of security risks in web applications