Abstract:
Cross-site scripting has caused considerable harm to the economy and
individual privacy. Deep learning consists of three primary learning
approaches, and it is made up of numerous strata of artificial neural networks.
Triggering functions that can be used for the production of non-linear outputs
are contained within each layer. This study proposes a secure framework that
can be used to achieve real-time detection and prevention of cross-site
scripting attacks in cloud-based web applications, using deep learning, with a
high level of accuracy. This project work utilized five phases cross-site
scripting payloads and Benign user inputs extraction, feature engineering,
generation of datasets, deep learning modeling, and classification filter for
Malicious cross-site scripting queries. A web application was then developed
with the deep learning model embedded on the backend and hosted on the
cloud. In this work, a model was developed to detect cross-site scripting
attacks using multi-layer perceptron deep learning model, after a comparative
analysis of its performance in contrast to three other deep learning models
deep belief network, ensemble, and long short-term memory. A multi-layer
perceptron based performance evaluation of the proposed model obtained an
accuracy of 99.47%, which shows a high level of accuracy in detecting crosssite scripting attacks
