Automated Network Enumeration and Vulnerability Analysis Tool: A Web-Based Approach with AI-Assisted Vulnerability Assessment

Robust network security requires accurate host enumeration and timely vulnerability detection. To simplify these tasks for both novice and professional users, this paper presents an automated web-based vulnerability assessment tool that integrates advanced Nmap scanning with real-time analysis and artificial intelligence assistance. Developed using the Flask framework with an SQLite backend, the system provides guest quick-scan access and authenticated deep-scan functionality, supports multiple scan execution with history management, and correlates findings with the National Vulnerability Database. The platform also incorporates DNS-over-HTTPS resolution and an AI-powered chatbot that interprets scan results in plain language and recommends remediation steps. Experimental evaluation demonstrates scan completion within five seconds for quick scans and between fifteen and thirty seconds for deep scans. The system achieves significant improvement in usability compared with traditional command-line tools while maintaining strong detection performance. This scalable platform bridges the accessibility gap in network security and provides a practical solution for ethical hacking, network auditing, and cybersecurity education.