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

Summary

The paper proposes an XSS attack detection model that uses two-stage Abstract Syntax Tree (AST) analysis, first extracting JavaScript from the HTML AST and then identifying malicious fragments in the JavaScript AST, with a Long Short-Term Memory (LSTM) network making the final classification. The authors report 0.991 accuracy and 0.998 F1 on standard XSS samples. Against adversarial XSS samples, their model keeps a detection rate above 0.982, while most existing models drop below 0.880.

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