Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems
A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances...
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3081 |
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| author | Latifah Almuqren Mashael S. Maashi Mohammad Alamgeer Heba Mohsen Manar Ahmed Hamza Amgad Atta Abdelmageed |
| author_facet | Latifah Almuqren Mashael S. Maashi Mohammad Alamgeer Heba Mohsen Manar Ahmed Hamza Amgad Atta Abdelmageed |
| author_sort | Latifah Almuqren |
| collection | DOAJ |
| description | A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%. |
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| format | Article |
| id | doaj.art-54e1ff0aa40d4759907f66f30273b6c5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:38Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-54e1ff0aa40d4759907f66f30273b6c52023-11-17T07:18:55ZengMDPI AGApplied Sciences2076-34172023-02-01135308110.3390/app13053081Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical SystemsLatifah Almuqren0Mashael S. Maashi1Mohammad Alamgeer2Heba Mohsen3Manar Ahmed Hamza4Amgad Atta Abdelmageed5Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi ArabiaDepartment of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaA cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.https://www.mdpi.com/2076-3417/13/5/3081securityintrusion detectioncyber-physical systemsexplainable artificial intelligencefeature selection |
| spellingShingle | Latifah Almuqren Mashael S. Maashi Mohammad Alamgeer Heba Mohsen Manar Ahmed Hamza Amgad Atta Abdelmageed Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems Applied Sciences security intrusion detection cyber-physical systems explainable artificial intelligence feature selection |
| title | Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems |
| title_full | Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems |
| title_fullStr | Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems |
| title_full_unstemmed | Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems |
| title_short | Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems |
| title_sort | explainable artificial intelligence enabled intrusion detection technique for secure cyber physical systems |
| topic | security intrusion detection cyber-physical systems explainable artificial intelligence feature selection |
| url | https://www.mdpi.com/2076-3417/13/5/3081 |
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