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Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure

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submitted on 2025-05-01, 07:58 and posted on 2025-05-01, 08:20 authored by Duaa Shoukat, Adnan AkhunzadaAdnan Akhunzada, Muhammad Taimoor Khan, Ahmad Sami Al-Shamayleh, Mueen UddinMueen Uddin, Hashem Alaidaros

The rapid evolution of Smart Infrastructure (SI) on a global scale has revolutionized our daily lives, empowering us with unprecedented connectivity and convenience. However, this evolution has also exposed smart devices to increasingly sophisticated cyber-threats, endangering the integrity of entire smart networks. In response to these challenges, this paper proposes a novel approach utilizing Deep Learning (DL) models for multi-class threat detection in SI environments. Specifically, we introduce the Cu-GRULSTM model, which leverages CUDA-enabled Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) architecture. Additionally, we employ the Cu-GRUDNN model for comparative analysis. Both models are trained and evaluated using the efficient and publicly available CICIDS2018 dataset. Our evaluation results demonstrate the superior performance of the proposed Cu-GRULSTM model, achieving an exceptional accuracy rate of 99.62% with a minimal False Alarms Rate (FAR) of 0.0003. This significant improvement over existing models underscores the efficacy of our approach in mitigating cyber-threats in smart infrastructure environments.

Other Information

Published in: Proceedings of the 1st International Conference on Creativity, Technology, and Sustainability
License: https://creativecommons.org/licenses/by/4.0
See chapter on publisher's website: https://doi.org/10.1007/978-981-97-8588-9_11

History

Language

  • English

Publisher

Springer Singapore

Publication Year

  • 2025

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Computing and Information Technology - UDST

Related Publications

Khalil, U., Uddin, M., Alaidaros, H., & Akhunzada, A. (2025). NFTs for the Unassailable Authentication of IoT Devices in Cyber-Physical Systems: An Implementation Study. Proceedings of the 1st International Conference on Creativity, Technology, and Sustainability, 87–95. https://doi.org/10.1007/978-981-97-8588-9_9

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