The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Transfer learning-based Mirai botnet detection in IoT networks
Abstract
The Internet of Things (IoT) has expeditiously evolved from a recent concept to a prevalent and often crucial side of diverse environments, including industry, healthcare, homes and buildings, etc. Its applications for almost all the fields have actually become a principal target for various threats, mainly botnets. To mitigate this recent threat, diverse techniques were developed, particularly those founded on Machine Learning, Deep Learning and Transfer Learning which have achieved significant results in detecting IoT botnets. In our paper we propose a transfer learning intrusion detection system (IDS) to identify IoT botnets, comparing between deep learning and transfer learning models using two different algorithms, Artificial Neural Network (ANN) and Recurrent Neural Network (RNN). Results prove that the suggested approach based TL reaches high accuracy up to 99% that overcomes the accuracy of DL model for the two algorithms.
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Subsequent Mirai variants and other IoT-botnet families continue to reproduce this pattern at scale.
“Subsequent Mirai variants and other IoT-botnet families continue to reproduce this pattern at scale”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [11]
Citations
Cites
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
Kim, J., Shim, M., Hong, S., Shin, Y., & Choi, E. (2020) · Applied Sciences · doi:10.3390/app10197009 · Cited by 106
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Research works printed but not linked: 13
- 2022, 15+ Shocking botnet statistics and facts for 2022
- 10.14704/web/v18si05/web18220 · DOI
- 10.3233/JIFS-191432 · DOI
- 10.23919/ICITST51030.2020.9351317 · DOI
- 10.1109/ACCESS.2020.3000476 · DOI
- 10.3390/s22155621 · DOI
- Khanday, 2023, Towards the Development of an Ensemble Intrusion Detection Model for DDoS and Botnet Mitigation using the IoT-23 Dataset, Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 44, 562
- 10.32890/jict2022.21.2.3 · DOI
- 10.1007/978-3-030-76776-1_2 · DOI
- 10.1109/ARGENCON.2016.7585247 · DOI
- 10.1109/TNSM.2022.3200741 · DOI
- 10.1007/s11277-022-10022-7 · DOI
- 10.1023/A:1010920819831 · DOI · Paper page
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Nageshwaran, V., & Ezekiel, S. (2026) · Electronics · doi:10.3390/electronics15122740 · Cited by 4
Metadata
- Authors
- Rabhi, S., Abbes, T., & Zarai, F.
- Affiliations
- University of Sfax (TN)
- Venue
- 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)
- Publisher
- IEEE
- Published
- 2023-09-20
- Pages
- 1-5
- OpenAlex
- W4388916306
- Cited by
- 4
- References
- 2
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1
- FWCI
- 1.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Innovations in Intelligent Systems and Applications (INISTA) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- Mirai botnet, IoT botnet detection, transfer learning, intrusion detection systems, Internet of Things security, recurrent neural networks, artificial neural networks, deep learning model comparison
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