Web Application Security Enhancement Through Automated Form Analysis and AI-Driven Attack Detection

Abstract

Securing web applications has grown essential due to the growing threat of cyberattacks such SQL Injection (SQLi), Cross-Site Scripting (XSS), and XML Injection. A web vulnerability scanner based on AI that scans and corrects such vulnerabilities automatically is suggested in this study. The suggested solution enables the proprietors of websites to provide the source code of their websites in compressed (ZIP) format, which is un-packed to strip form inputs and scan them for possible security weaknesses. A pre-trained Bidirectional Long Short-Term Memory (BiLSTM) model is used to scan for XSS and XML-based attacks and identify SQLi vulnerabilities. The system blocks attackers automatically, initiates a comprehensive security code analysis, and creates comprehensive reports for inspection when it detects malicious inputs. The web site owners are also offered access to an interactive dashboard through which they can track security incidents and risk scores, and all attack records are kept systematically for reuse. To demonstrate how effectively the proposed security solution works in improving web application security and foreseeing cyber threats proactively, the architecture, behavior, and performance of this article detail it.