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

ORCID

0009-0007-3728-1634 (Harahsheh)

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