XSSer: hybrid deep learning for enhanced cross-site scripting detection - Odeh, Ammar; Abu Taleb, Anas (Bulletin of Electrical Engineering and Informatics, 2024)
The importance of an effective cross-site scripting (XSS) detection system cannot be overstated in web security. XSS attacks continue to be a prevalent and severe threat to web applications, making the need for robust detection systems more crucial than ever. This paper introduced a hybrid model that leverages deep learning algorithms, combining recurrent neural network (RNN) and convolutional neural network (CNN) architectures. Our hybrid RNN-CNN model emerged as the top performer in our evaluation, demonstrating outstanding performance across key metrics. It achieved an impressive accuracy of 96.74%, excelling inaccurate predictions. Notably, the precision score reached an impressive 97.78%, highlighting its precision in identifying positive instances while minimizing false positives. Furthermore, the model's recall score of 95.65% showcased its ability to capture a substantial portion of true positive instances. This resulted in an exceptional F1-Score of 96.70, underlining the model's remarkable balance between precision and recall. Compared to other models in the evaluation, our proposed model unequivocally demonstrated its leadership, emphasizing its excellence in detecting potential XSS vulnerabilities within web content.