An Innovative Botnet Revelation Framework for Competing Concerns in IoT (BRF-CCIoT)

Priyang P. Bhatt, Bhaskar Thakker () and Falgun Thakkar ()
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Priyang P. Bhatt: G H Patel College of Engineering and Technology, The Charutar Vidya Mandal (CVM) University
Bhaskar Thakker: G H Patel College of Engineering and Technology, The Charutar Vidya Mandal (CVM) University
Falgun Thakkar: G H Patel College of Engineering and Technology, The Charutar Vidya Mandal (CVM) University

A chapter in Industry 5.0, 2025, pp 479-504 from Springer

Abstract: Abstract The Internet of Things (IoT) has dramatically increased the number of connected devices communicating through messaging bots. While these bots are essential for automating and managing workflows, attackers can also use them to perform malicious activities on IoT devices, posing a significant cybersecurity threat. In this regard, detecting the presence of malicious bots on the network is crucial. This paper presents an Innovative Botnet Revelation Framework for Competing Concerns in IoT (BRF-CCIoT) that uses Stream Mining to generate instances with minimal memory and time. The framework uses adaptive Naive Bayes (NB) to accurately identify botnets by analyzing network streams. The proposed method achieves high performance with a small number of labeled instances, as evidenced by its accuracy, precision, recall, and F1 scores. The results show that the proposed method can effectively detect and prevent botnet attacks in IoT systems.

Keywords: IoT; Botnet detection; Botnet; Adaptive Naïve Bayes; Instance creation; Cataloging (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-87837-4_20

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DOI: 10.1007/978-3-031-87837-4_20

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