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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Marion Adebiyi
Landmark University
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning Isaac Odun- Ayo; Williams Toro- Abasi; Marion Adebiyi; Oladapo Alagbe
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3168

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 cross-site scripting attacks.
Cloud middleware and services-a systematic mapping review Isaac Odun-Ayo; Marion Adebiyi; Olatunji Okesola; Olufunke Vincent
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3169

Abstract

Cloud computing currently plays a crucial role in the delivery of vital information technology services. A unique aspect of cloud computing is the cloud middleware and other related entities that support applications and networks. A specific field of research may be considered, particularly as regards cloud middleware and services at all levels, and thus needs analysis and paper surveys to elucidate possible study limitations. The purpose of this paper is to perform a systematic mapping for studies that capture cloud computing middleware, stacks, tools and services. The methodology adopted for this study is a systematic mapping review. The results showed that more papers on the contribution facet were published with tool, model, method and process having 18.10%, 13.79%, 6.03% and 8.62% respectively. In addition, in terms of tool, evaluation and solution research had the largest number of articles with 14.17% and 26.77% respectively. A striking feature of the systemic map is the high number of articles in solution research with respect to all aspects of the features applied in the studies. This study showed clearly that there are gaps in cloud computing middleware and delivery services that would interest researchers and industry professionals desirous of research in this area.
Co-Authors Adeyinka Adewale Ayodele Adebiyi Isaac Odun- Ayo Isaac Odun-Ayo Julius Olatunji Okesola Oladapo Alagbe Olatunji Okesola Olufunke Vincent Tochukwu Osi-Okeke Williams Toro- Abasi