An improved man-in-the-middle (MITM) attack detections using convolutional neural networks

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dc.contributor.authorIddrisu, Mohammed
dc.contributor.authorTakyi, Kate
dc.contributor.authorGyening, Rose-Mary Owusuaa Mensah
dc.contributor.authorPeasah, Kwame Ofosuhene
dc.contributor.authorBanning, Linda Amoako
dc.contributor.authorAgyemang, Kwabena Owusu
dc.contributor.orcid0000-0002-8087-5207
dc.date.accessioned2024-11-14T10:05:41Z
dc.date.available2024-11-14T10:05:41Z
dc.date.issued2024-08
dc.descriptionThis article is published by Multidisciplinary Science Journal, 2024
dc.description.abstractThe increasing reliance on digital communication networks has made information security a critical concern for individuals, organizations, and governments worldwide. Man-in-the-middle (MITM) attacks are significant, prevalent, and damaging concerning cyber-attacks. Detecting MitM attacks is complex due to their stealthy nature and the sophisticated methods employed by attackers. There is the need for researchers to address this issue using current and novel methods like artificial intelligence. In this paper, an improved MitM attack detection approach using the Convolutional Neural Network (CNN) deep learning algorithm is developed, resulting in an overall detection accuracy of 0.986%. The results confirms that the proposed model is very efficient in comparision to other proposed solutions by other authors.
dc.description.sponsorshipKNUST
dc.identifier.citationMultidiciplainary Science Journal, 2024
dc.identifier.uri10.31893/multiscience.2025129
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/15942
dc.language.isoen
dc.publisherMultidiciplainary Science Journal
dc.titleAn improved man-in-the-middle (MITM) attack detections using convolutional neural networks
dc.typeArticle
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