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Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring.

Zakariyya, Idris; Kalutarage, Harsha; Al-Kadri, M. Omar

Authors

Idris Zakariyya

M. Omar Al-Kadri



Abstract

The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenges due to computational intensity and vulnerability to adversarial samples. To address these issues, this paper introduces an optimization method that combines regularization and simulated micro-batching. This approach enables the training of DNNs in a robust, efficient, and resource-friendly manner for IoT security monitoring. Experimental results demonstrate that the proposed DNN model, including its performance in Federated Learning (FL) settings, exhibits improved attack detection and resistance to adversarial perturbations compared to benchmark baseline models and conventional Machine Learning (ML) methods typically employed in IoT security monitoring. Notably, the proposed method achieves significant reductions of 79.54% and 21.91% in memory and time usage, respectively, when compared to the benchmark baseline in simulated virtual worker environments. Moreover, in realistic testbed scenarios, the proposed method reduces memory footprint by 6.05% and execution time by 15.84%, while maintaining accuracy levels that are superior or comparable to state-of-the-art methods. These findings validate the feasibility and effectiveness of the proposed optimization method for enhancing the efficiency and robustness of DNN-based IoT security monitoring.

Citation

ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2023. Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. Computer and security [online], 133, article 103388. Available from: https://doi.org/10.1016/j.cose.2023.103388

Journal Article Type Article
Acceptance Date Jul 16, 2023
Online Publication Date Jul 20, 2023
Publication Date Oct 31, 2023
Deposit Date Jul 21, 2023
Publicly Available Date Jul 21, 2023
Journal Computers and Security
Print ISSN 0167-4048
Electronic ISSN 1872-6208
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 133
Article Number 103388
DOI https://doi.org/10.1016/j.cose.2023.103388
Keywords Internet of Things; Deep neural networks; Cybersecurity; Resource constrained; Attack detection; Federated learning
Public URL https://rgu-repository.worktribe.com/output/2015652
Related Public URLs https://rgu-repository.worktribe.com/output/1987917 (Thesis by Idris Zakariyya)

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