WEB VULNERABILITY SCANNER

Web applications form the backbone of digital services across sectors such as finance, healthcare, e-commerce, and education. However, their rapid growth has also increased the attack surface exploited by cybercriminals. Common vulnerabilities—SQL Injection (SQLi), Cross-Site Scripting (XSS), Broken Authentication, Insecure Deserialization, and Server Misconfigurations—continue to appear in the OWASP Top 10. Existing web vulnerability scanners attempt to automate detection, yet most fail to achieve reliable accuracy due to outdated payload databases, limited crawling depth, inability to analyze JavaScript-rich pages, and poor performance on modern frameworks like React, Angular. This research presents an enhanced Web Vulnerability Scanner that integrates adaptive payload generation, dynamic crawling using headless browsers, API-aware scanning, and a machine learning–based classification system to minimize false positives. The proposed system addresses gaps identified in IEEE, ACM, Springer, and MDPI studies. Through modular architecture, improved detection coverage, and modern framework compatibility, the system aims to strengthen automated web security testing and contribute to both academic and practical cybersecurity advancements. Keywords: Web Vulnerability Scanner, Web Application Security, API-Aware Scanning, Adaptive Payloads, SQL Injection, Cross-Site Scripting, Automated Testing, Cybersecurity, OWASP, Chatbot Assistance.