Abstract
The rapid growth of Internet of Things (IoT) devices has changed human interactions with the environment. IoT networks require specialized defense strategies distinct from traditional corporate contexts. Security measures such as anti-malware software, firewalls, authentication protocols, and encryption techniques are established but face limitations against evolving attack strategies. Therefore, this study proposes an intrusion detection approach for a realistic IoT environment, employing various variants of deep learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The research tested three variants for each model: DNN1, DNN2, DNN3, CNN1, CNN2, CNN3, RNN1, RNN2, RNN3. All these variants are customized and tuned differently to analyze the efficacy of the suggested methodology. Likewise, notable observation highlights the significance of aligning training and validation accuracy curves that indicate controlled overfitting, validating the model’s reliability in accurately predicting intrusion and benign network traffic in IoT settings. Results reveal that RNN1 achieves the best results: accuracy of 98.61%, precision of 98.55%, recall of 98.61%, and F1-score of 98.57% compared with other DNN and CNN architectures and benchmark papers. This study advances intrusion detection within IoT networks through a comprehensive evaluation of deep learning models and inspires ongoing research to enhance intrusion detection systems’ resilience in dynamic IoT environments.
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Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for supporting this research.
Funding
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under Grant Number RGP2/470/44.
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“Conceptualization, Sidra Abbas, Gabriel Avelino Sampedro; Data curation, Sidra Abbas, Ahmad Almadhor; Formal analysis, Sidra Abbas, Gabriel Avelino Sampedro, Stephen Ojo, Shtwai Alsubai, Ahmad Almadhor, Imen Bouazzi , Abdullah Al Hejaili; Funding acquisition, Abdullah Al Hejaili; Investigation, Gabriel Avelino Sampedro, Stephen Ojo, Ahmad Almadhor, Imen Bouazzi, Abdullah Al Hejaili; Methodology, Sidra Abbas, Abdullah Al Hejaili, Gabriel Avelino Sampedro; Supervision, Sidra Abbas, Imen Bouazzi, Abdullah Al Hejaili; Validation, Gabriel Avelino Sampedro, Stephen Ojo, Shtwai Alsubai, Ahmad Almadhor, Imen Bouazzi, Abdullah Al Hejaili, Sidra Abbas; Visualization, Sidra Abbas; Writing Origional Draft, Gabriel Avelino Sampedro, Stephen Ojo, Shtwai Alsubai, Ahmad Almadhor, Imen Bouazzi , Abdullah Al Hejaili, Sidra Abbas; Writing – review & editing, Gabriel Avelino Sampedro, Stephen Ojo, Shtwai Alsubai, Ahmad Almadhor, Imen Bouazzi , Abdullah Al Hejaili, Sidra Abbas;
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Abbas, S., Alsubai, S., Ojo, S. et al. An efficient deep recurrent neural network for detection of cyberattacks in realistic IoT environment. J Supercomput 80, 13557–13575 (2024). https://doi.org/10.1007/s11227-024-05993-2
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DOI: https://doi.org/10.1007/s11227-024-05993-2