Survey on Vulnerability Detection in Web Environment Using Tools, Machine Learning, Deep Learning Techniques

Increasing dependence on web applications has raised serious security concerns, especially in identifying weaknesses that can be exploited by attackers. Although powerful, traditional vulnerability detection methods often fail to address newly emerging threats. Machine learning (ML) and Deep Learning (DL) approaches have recently promised to increase vulnerable identity by looking at trends and abnormalities that can ignore traditional approaches. This report provides an intensive analysis of the current status of detection tools and ML, DL techniques used in web environments. We compare how well different ways—such as machine learning models, automated scanning tools, and stable and dynamic analysis—identify security threats. To enhance vulnerability detection, we also explore the challenges and potential strategies for integrating machine learning with traditional safety techniques. Our findings highlight the strengths and boundaries of the current approaches and provide insight to researchers aiming to develop stronger web security solutions.