Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment
A cyber-physical system (CPS) incorporates many interconnected physical processes, networking units, and computing resources, along with monitoring the application and process of the computing system. Interconnection of the cyber and physical world initiates threatening security problems, particular...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925003533 |
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| author | Mimouna Abdullah Alkhonaini Nouf Aljaffan Yahia Said Jamal Alsamri Nadhem Nemri Marwa Obayya Abdulaziz A. Alzubaidi Yazan A. Alsariera Mrim M. Alnfiai |
| author_facet | Mimouna Abdullah Alkhonaini Nouf Aljaffan Yahia Said Jamal Alsamri Nadhem Nemri Marwa Obayya Abdulaziz A. Alzubaidi Yazan A. Alsariera Mrim M. Alnfiai |
| author_sort | Mimouna Abdullah Alkhonaini |
| collection | DOAJ |
| description | A cyber-physical system (CPS) incorporates many interconnected physical processes, networking units, and computing resources, along with monitoring the application and process of the computing system. Interconnection of the cyber and physical world initiates threatening security problems, particularly with the increasing sophistication of transmission networks. Analyzing and detecting cyber-physical attacks in complex CPS systems remains a threat. Researchers have turned to machine learning (ML) for cyber-physical security evaluation. Recent enhancements in deep learning (DL) and artificial intelligence (AI) enable the development of robust intrusion detection systems (IDS) for CPS platforms. A metaheuristic algorithm is employed for feature selection (FS) to mitigate the curse of dimensionality. The study introduces an Osprey Optimization Algorithm with Deep Ensemble Learning for Cybersecurity (OOADEL-CS) in the CPS platform. The proposed OOADEL-CS method identifies and classifies intrusions in the CPS platform. In the OOADEL-CS method, the linear scaling normalization (LSN) approach is utilized for uniform data scaling. For FS, the OOADEL-CS technique employs the OOA to select a subset of features. To detect the intrusions effectually, the OOADEL-CS technique utilizes an ensemble of three models, namely bidirectional long short-term memory (BiLSTM), autoencoder (AE), and multi-layer perceptron (MLP). A modified bacterial foraging optimization algorithm (MBFOA) approach improves the detection classifier rate. The simulation analysis of the OOADEL-CS approach is conducted on a benchmark dataset. The experimental validation of the OOADEL-CS approach portrayed a superior accuracy value of 99.47 % over existing methods. |
| format | Article |
| id | doaj-art-4ff8d566c50648889fa5d3a37a1f8f48 |
| institution | Matheson Library |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-4ff8d566c50648889fa5d3a37a1f8f482025-07-11T04:31:10ZengElsevierAin Shams Engineering Journal2090-44792025-10-011610103612Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environmentMimouna Abdullah Alkhonaini0Nouf Aljaffan1Yahia Said2Jamal Alsamri3Nadhem Nemri4Marwa Obayya5Abdulaziz A. Alzubaidi6Yazan A. Alsariera7Mrim M. Alnfiai8Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Saudi ArabiaDepartment of Computer Science and Engineering, College of Applied Science and Community Services, King Saud University, PO Box 103786, Riyadh 11543, Saudi ArabiaCenter for Scientific Research and Entrepreneurship, Northern Border University, 73213, Arar, Saudi Arabia; Corresponding authors.Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Systems, Applied College at Mahayil, King Khalid University, Saudi ArabiaDepartment of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, College of Engineering and Computing in Al-qunfudah, Umm Al-qura University, Saudi ArabiaDepartment of Computer Science, College of Information and Communications Technology, Tafila Technical University, Tafila 66110, JordanDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaA cyber-physical system (CPS) incorporates many interconnected physical processes, networking units, and computing resources, along with monitoring the application and process of the computing system. Interconnection of the cyber and physical world initiates threatening security problems, particularly with the increasing sophistication of transmission networks. Analyzing and detecting cyber-physical attacks in complex CPS systems remains a threat. Researchers have turned to machine learning (ML) for cyber-physical security evaluation. Recent enhancements in deep learning (DL) and artificial intelligence (AI) enable the development of robust intrusion detection systems (IDS) for CPS platforms. A metaheuristic algorithm is employed for feature selection (FS) to mitigate the curse of dimensionality. The study introduces an Osprey Optimization Algorithm with Deep Ensemble Learning for Cybersecurity (OOADEL-CS) in the CPS platform. The proposed OOADEL-CS method identifies and classifies intrusions in the CPS platform. In the OOADEL-CS method, the linear scaling normalization (LSN) approach is utilized for uniform data scaling. For FS, the OOADEL-CS technique employs the OOA to select a subset of features. To detect the intrusions effectually, the OOADEL-CS technique utilizes an ensemble of three models, namely bidirectional long short-term memory (BiLSTM), autoencoder (AE), and multi-layer perceptron (MLP). A modified bacterial foraging optimization algorithm (MBFOA) approach improves the detection classifier rate. The simulation analysis of the OOADEL-CS approach is conducted on a benchmark dataset. The experimental validation of the OOADEL-CS approach portrayed a superior accuracy value of 99.47 % over existing methods.http://www.sciencedirect.com/science/article/pii/S2090447925003533Cyber-Physical SystemEnsemble LearningCybersecurityOsprey Optimization AlgorithmFeature Selection |
| spellingShingle | Mimouna Abdullah Alkhonaini Nouf Aljaffan Yahia Said Jamal Alsamri Nadhem Nemri Marwa Obayya Abdulaziz A. Alzubaidi Yazan A. Alsariera Mrim M. Alnfiai Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment Ain Shams Engineering Journal Cyber-Physical System Ensemble Learning Cybersecurity Osprey Optimization Algorithm Feature Selection |
| title | Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment |
| title_full | Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment |
| title_fullStr | Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment |
| title_full_unstemmed | Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment |
| title_short | Leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in CPS environment |
| title_sort | leveraging osprey optimization algorithm with deep ensemble learning for cybersecurity in cps environment |
| topic | Cyber-Physical System Ensemble Learning Cybersecurity Osprey Optimization Algorithm Feature Selection |
| url | http://www.sciencedirect.com/science/article/pii/S2090447925003533 |
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