Cloud-Cyber Physical Systems: Enhanced Metaheuristics with Hierarchical Deep Learning-based Cyberattack Detection

Authors

  • Ahmad Taher Azar College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia | Faculty of Computers and Artificial Intelligence, Benha University, Egypt
  • Syed Umar Amin Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia | College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
  • Mohammed Abdul Majeed Department of Cybersecurity and Cloud Computing Technical Engineering, Uruk University, Baghdad, Iraq
  • Ahmed Al-Khayyat College of Technical Engineering, The Islamic University of Najaf, Iraq | College of Technical Engineering, The Islamic University of Al Diwaniyah, Iraq | College of Technical Engineering, The Islamic University of Babylon, Iraq
  • Ibraheem Kasim Department of Electrical Engineering, College of Engineering, University of Baghdad, Iraq
Volume: 14 | Issue: 6 | Pages: 17572-17583 | December 2024 | https://doi.org/10.48084/etasr.8286

Abstract

Cyber-Physical Systems (CPS) integrate several interconnected physical processes, networking units, and computing resources, along with monitoring the processes of the computing system. The connection between the cyber and physical world creates threatening security problems, particularly with the growing complexities of transmission networks. Despite efforts to overcome this challenge, it remains challenging to analyze and detect cyber-physical attacks in CPS. This study mainly focuses on the development of Enhanced Metaheuristics with Hierarchical Deep Learning-based Attack Detection (EMHDL-AD) method in a cloud-based CPS environment. The proposed EMHDL-AD method identifies various types of attacks to protect CPS. In the initial stage, data preprocessing is implemented to convert the input dataset into a useful format. Then, the Quantum Harris Hawks Optimization (QHHO) algorithm is used for feature selection. An Improved Salp Swarm Algorithm (ISSA) is used to optimize the hyperparameters of the HDL technique to recognize several attacks. The performance of the EMHDL-AD algorithm was examined using two benchmark intrusion datasets, and the experimental results indicated improvements over other existing approaches.

Keywords:

cyber-physical systems, hierarchical deep learning, attack detection, enhanced metaheuristics, feature selection

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How to Cite

[1]
Azar, A.T., Amin, S.U., Majeed, M.A., Ahmed Al-Khayyat and Kasim, I. 2024. Cloud-Cyber Physical Systems: Enhanced Metaheuristics with Hierarchical Deep Learning-based Cyberattack Detection. Engineering, Technology & Applied Science Research. 14, 6 (Dec. 2024), 17572–17583. DOI:https://doi.org/10.48084/etasr.8286.

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