Comparing the Detection of XSS Vulnerabilities in Node.js and a Multi-tier JavaScript-based Language via Deep Learning

MAUREL, HELOISE; VIDAL, SANTIAGO A.; REZK, TAMARA

Conferencia; 8th International Conference on Information Systems Security and Privacy; 2022

Cross-site Scripting (XSS) is one of the most common and impactful software vulnerabilities (ranked second in the CWE ?s top 25 in 2021). Several approaches have focused on automatically detecting software vulnera- bilities through machine learning models. To build a model, it is necessary to have a dataset of vulnerable and non-vulnerable examples and to represent the source code in a computer understandable way. In this work, we explore the impact of predicting XSS using representations based on single-tier and multi-tier languages. We built 144 models trained on Javascript-based multitier code - i.e. which includes server code and HTML, Javascript and CSS as client code - and 144 models trained on single-tier code, which include sever code and client-side code as text. Despite the lower precision, our results show a better recall with multitier languages than a single-tier language, implying an insignificant impact on XSS detectors based on deep learning.