Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems


  • C. Atheeq GITAM University, India
  • Ruhiat Sultana Lords Institute of Engineering and Technology, India
  • Syeda Asfiya Sabahath King Khalid University, Saudi Arabia
  • Murtuza Ahmed Khan Mohammed Universiti Teknologi Malaysia
Volume: 14 | Issue: 2 | Pages: 13559-13566 | April 2024 |


Securing Internet of Things (IoT)-enabled Cyber-Physical Systems (CPSs) can be challenging because security solutions intended for typical IT/OT systems may not be as effective in a CPS setting. The goal of this study is to create a mechanism for identifying and attributing two-level ensemble attacks that are specifically designed for use against Industrial Control Systems (ICSs). An original ensemble deep representation learning model is combined with decision tree algorithm to identify assaults on unbalanced ICS environments at the first level. An attack attribution network, which constitutes a collection of deep neural networks, is formed at the second level. The proposed model is tested using real-world datasets, notably those pertaining to water purification and gas pipelines. The results demonstrate that the proposed strategy outperforms other strategies with comparable computing complexity and that the recommended model outperforms the existing mechanisms.


cyber-attacks, deep learning, threat detection, industrial control system, industrial IoT, cyber-physical systems


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How to Cite

C. Atheeq, R. Sultana, S. A. Sabahath, and M. A. K. Mohammed, “Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13559–13566, Apr. 2024.


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