An improved man-in-the-middle (MITM) attack detections using convolutional neural networks
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Date
2024-08
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Multidiciplainary Science Journal
Abstract
The 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.
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This article is published by Multidisciplinary Science Journal, 2024
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Multidiciplainary Science Journal, 2024