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

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

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)