BEYOND STATIC ANALYSIS: AI TECHNIQUES FOR DETECTING WEB APPLICATION SECURITY WEAKNESSES

Abstract

The increasing complexity of modern web applications has exposed critical limitations in traditional static analysis tools, which often fail to detect dynamic, context-dependent, or obfuscated security vulnerabilities. Recent advancements in Artificial Intelligence (AI) provide promising new directions for improving automated security assessment. This study explores machine learning–driven and deep learning–driven approaches for identifying web application weaknesses beyond static analysis. We examine AI-based anomaly detection, dynamic behavior modeling, semantic code understanding, and hybrid intelligent scanning methods capable of detecting SQL injection, XSS, authentication bypass, and logical vulnerabilities. The proposed framework integrates static features with runtime behavioral signals to enhance detection accuracy and reduce false positives. The findings demonstrate that AI-enabled methods outperform traditional scanners in identifying sophisticated attack patterns and zero-day indicators, marking a significant advancement in intelligent web security assessment.