Web-based Vulnerability Analysis and Detection

Abstract

Listen

Translate article

Background: In today’s digital world, web-based applications are widely used across organizations, making them a primary target for breaches, hacking attempts, data theft, and unauthorized access. Ensuring robust security through vulnerability analysis and detection is critical to mitigate emerging threats and prevent potential exploits. Objective: This research aims to develop and demonstrate a novel approach to web-based vulnerability assessment by combining advanced automated scanning tools with human expertise. The study introduces a modular, flexible, and efficient scanning tool capable of detecting critical vulnerabilities in web applications. Methods: The proposed tool is built using Python and Selenium and employs a modular architecture with regular expression-based detection techniques. The scanner identifies vulnerabilities such as SQL injection, cross-site scripting (XSS), file inclusions, command injection, and more. A ranking framework is introduced to prioritize fixes based on exploiting potential, severity, and patch availability. The tool's performance was evaluated on real-world web applications to assess its accuracy, detection speed, and effectiveness compared to traditional methods. Results: Experimental results highlight significant improvements in vulnerability detection accuracy and scanning speed. The tool successfully identified a broad range of vulnerabilities, including complex and dynamic ones, outperforming traditional scanning techniques. The modular design and automation enabled faster and more flexible vulnerability detection across diverse web platforms. Conclusion: This research provides a holistic and practical approach to web-based vulnerability assessment, combining advanced automated scanning with human analysis for optimal results. The introduced tool offers a reliable solution for detecting and prioritizing vulnerabilities, empowering organizations to enhance their cybersecurity defenses. By improving detection accuracy and efficiency, this study contributes to advancing web security practices and provides actionable insights for organizations navigating the evolving digital landscape.