An XSS Attack Detection Model Based on Two-Stage AST Analysis

XSS attacks (malicious code injected into websites to steal user data) are hard to detect because attackers can create adversarial samples that trick detection models into missing threats. This paper proposes a new detection model using two-stage AST (abstract syntax tree, a structural representation of code) analysis combined with LSTM (long short-term memory, a type of neural network good at processing sequences) to better identify malicious code while resisting adversarial tricks, achieving over 98.2% detection accuracy even against adversarial attacks.