Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks

Bright Agbor Agbor; Bliss Utibe-Abasi Stephen; Philip Asuquo; Uduak Onofiok Luke; Victor Anaga

doi:10.3390/computers14020058

journal article Open Access Feb 10, 2025

Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks

Bright Agbor Agbor

Bliss Utibe-Abasi Stephen

Philip Asuquo

Uduak Onofiok Luke Victor Anaga

Computers Vol. 14 No. 2 pp. 58 · MDPI AG

View at Publisher Save 10.3390/computers14020058

Abstract

The Internet of Things (IoT) ecosystem is rapidly expanding. It is driven by continuous innovation but accompanied by increasingly sophisticated cybersecurity threats. Protecting IoT devices from these emerging vulnerabilities has become a critical priority. This study addresses the limitations of existing IoT threat detection methods, which often struggle with the dynamic nature of IoT environments and the growing complexity of cyberattacks. To overcome these challenges, a novel hybrid architecture combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Deep Neural Networks (DNN) is proposed for accurate and efficient IoT threat detection. The model’s performance is evaluated using the IoT-23 and Edge-IIoTset datasets, which encompass over ten distinct attack types. The proposed framework achieves a remarkable 99% accuracy on both datasets, outperforming existing state-of-the-art IoT cybersecurity solutions. Advanced optimization techniques, including model pruning and quantization, are applied to enhance deployment efficiency in resource-constrained IoT environments. The results highlight the model’s robustness and its adaptability to diverse IoT scenarios, which address key limitations of prior approaches. This research provides a robust and efficient solution for IoT threat detection, establishing a foundation for advancing IoT security and addressing the evolving landscape of cyber threats while driving future innovations in the field.

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Details

Published: Feb 10, 2025
Vol/Issue: 14(2)
Pages: 58
License: View

Authors

B

Bright Agbor Agbor

Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria

B

Bliss Utibe-Abasi Stephen

Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria

P

Philip Asuquo

Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria

U

Uduak Onofiok Luke

School of Sustainability, Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK

V

Victor Anaga

Department of Computer Engineering, University of Uyo, Uyo 520101, Nigeria

Cite This Article

Bright Agbor Agbor, Bliss Utibe-Abasi Stephen, Philip Asuquo, et al. (2025). Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks. Computers, 14(2), 58. https://doi.org/10.3390/computers14020058

Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks

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