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2024 | OriginalPaper | Chapter

Detection of Man-in-the-Middle Attack Through Artificial Intelligence Algorithm

Authors : Ahmet Nail Taştan, Serkan Gönen, Mehmet Ali Barışkan, Cemallettin Kubat, Derya Yıltaş Kaplan, Elham Pashaei

Published in: Advances in Intelligent Manufacturing and Service System Informatics

Publisher: Springer Nature Singapore

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Abstract

The chapter delves into the critical issue of Man-in-the-Middle (MitM) attacks in IoT and IIoT systems, highlighting the escalating cybersecurity concerns in these increasingly interconnected environments. It presents a comprehensive detection mechanism employing machine learning algorithms, with a particular focus on the Random Forest algorithm. The evaluation of various algorithms reveals the Random Forest algorithm's superior efficacy, achieving an accuracy rate of 99.2%, an F1 score of 0.976, and a precision score of 0.977. The study also provides insightful recommendations for both practitioners and researchers, emphasizing the need for continuous updates and inter-disciplinary collaborations to enhance IoT and IIoT security. The research underscores the importance of prioritizing innovative solutions to safeguard these systems against evolving cyber threats, making it a valuable resource for professionals seeking to strengthen their cybersecurity strategies.

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Title: Detection of Man-in-the-Middle Attack Through Artificial Intelligence Algorithm
Authors: Ahmet Nail Taştan
Serkan Gönen
Mehmet Ali Barışkan
Cemallettin Kubat
Derya Yıltaş Kaplan
Elham Pashaei
Publisher: Springer Nature Singapore
Book: Advances in Intelligent Manufacturing and Service System Informatics
Print ISBN: 978-981-9960-61-3

Electronic ISBN: 978-981-9960-62-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-6062-0_41

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