An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

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dc.contributor.authorOdun-Ayo, Isaac
dc.contributor.authorToro-Abasi, Williams
dc.date.accessioned2022-08-12T13:29:03Z
dc.date.available2022-08-12T13:29:03Z
dc.date.issued2021
dc.description.abstractCross-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 attacksen_US
dc.identifier.issn: 2302-9285
dc.identifier.urihttp://hdl.handle.net/123456789/1469
dc.language.isoenen_US
dc.subjectCross-site scriptingen_US
dc.subjectDeep learningen_US
dc.titleAn implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learningen_US
dc.typeArticleen_US
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