Enhancing cross-site scripting attack detection by using FastText as word embeddings and long-short term memory

Mashuri, Muhammad Alkhairi
Surantha, Nico

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 14, No 6: December 2025

Cross-site scripting (XSS) is one of the dangerous cyber-attacks and the number of attacks continues to increase. This study takes a new approach to detect attacks by utilizing FastText as word embedding, and long-short term memory (LSTM), which aims to improve the performance of deep learning. This method is proposed to capture the broader meaning and context of the data used, leading to better feature extraction and model performance. This study not only improves the detection of XSS attacks, but also highlights the potential for better text processing techniques. The results obtained showing this method achieves higher results than other methods, with an accuracy of 99.89%.