Document Type
Article
Publication Date
2024
DOI
10.14569/IJACSA.2024.0151204
Publication Title
Science and Information Organization
Volume
15
Issue
12
Pages
35-42
Abstract
The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was employed, while optimization techniques such as hyperparameter tuning, regularization, and batch normalization further improved model performance. Experimental evaluations on five diverse IoT datasets, i.e. Bot-IoT, N-BaIoT, TON_IoT, CICIDS 2017, and NSL-KDD, demonstrate that the framework achieves high accuracy (92%-94%) while maintaining scalability, computational efficiency, and data privacy. This approach provides a robust solution to real-time intrusion detection in resource-constrained IoT environments.
Rights
© 2024 The Authors.
This is an open access article licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Original Publication Citation
Harahsheh, K., Alzaqebah, M., & Chen, C.-H. (2024). An enhanced real-time intrusion detection framework using Federated Transfer Learning in large-scale IoT networks. International Journal of Advanced Computer Science and Applications, 15(12), 35-42. https://doi.org/10.14569/IJACSA.2024.0151204
Repository Citation
Harahsheh, Khawlah; Alzaqebah, Malek; and Chen, Chung-Hao, "An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning in Large-Scale IoT Networks" (2024). Electrical & Computer Engineering Faculty Publications. 497.
https://digitalcommons.odu.edu/ece_fac_pubs/497
ORCID
0009-0007-3728-1634 (Harahsheh)
Included in
Artificial Intelligence and Robotics Commons, Cybersecurity Commons, Electrical and Computer Engineering Commons