Statically identifying XSS using deep learning
Identifier statiquement des failles XSS à l'aide d'apprentissage en profondeur
Résumé
Cross-site Scripting (XSS) is ranked first in the top 25 Most Dangerous Software Weaknesses (2020) of Common Weakness Enumeration (CWE) and places this vulnerability as the most dangerous among programming errors. In this work, we explore static approaches to detect XSS vulnerabilities using neural networks. We compare two different code representations based on Natural Language Processing (NLP) and Programming
Language Processing (PLP) and experiment with models based on different neural network architectures for static analysis detection in PHP and Node.js. We train and evaluate the models using synthetic databases. Using the generated PHP and Node.js databases, we compare our results with a well-known static analyzer for PHP code, ProgPilot, and a known scanner for Node.js, AppScan static mode. Our analyzers using neural networks overcome the results of existing tools in all cases