Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems
Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4091 |
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| author | Louai A. Maghrabi Ibrahim R. Alzahrani Dheyaaldin Alsalman Zenah Mahmoud AlKubaisy Diaa Hamed Mahmoud Ragab |
| author_facet | Louai A. Maghrabi Ibrahim R. Alzahrani Dheyaaldin Alsalman Zenah Mahmoud AlKubaisy Diaa Hamed Mahmoud Ragab |
| author_sort | Louai A. Maghrabi |
| collection | DOAJ |
| description | Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques. |
| first_indexed | 2024-03-10T21:46:31Z |
| format | Article |
| id | doaj.art-4980cd41446245f498377040006836f3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:46:31Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4980cd41446245f498377040006836f32023-11-19T14:17:06ZengMDPI AGElectronics2079-92922023-09-011219409110.3390/electronics12194091Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things SystemsLouai A. Maghrabi0Ibrahim R. Alzahrani1Dheyaaldin Alsalman2Zenah Mahmoud AlKubaisy3Diaa Hamed4Mahmoud Ragab5Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah 21448, Saudi ArabiaComputer Science and Engineering Department, College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin 39524, Saudi ArabiaDepartment of Cybersecurity, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi ArabiaThe Management of Digital Transformation and Innovation Systems in Organization Research Group, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi ArabiaInformation Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaRecently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques.https://www.mdpi.com/2079-9292/12/19/4091Industry 4.0Industrial Internet of Thingscybersecurityintrusion detectionmetaheuristics |
| spellingShingle | Louai A. Maghrabi Ibrahim R. Alzahrani Dheyaaldin Alsalman Zenah Mahmoud AlKubaisy Diaa Hamed Mahmoud Ragab Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems Electronics Industry 4.0 Industrial Internet of Things cybersecurity intrusion detection metaheuristics |
| title | Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems |
| title_full | Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems |
| title_fullStr | Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems |
| title_full_unstemmed | Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems |
| title_short | Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems |
| title_sort | golden jackal optimization with a deep learning based cybersecurity solution in industrial internet of things systems |
| topic | Industry 4.0 Industrial Internet of Things cybersecurity intrusion detection metaheuristics |
| url | https://www.mdpi.com/2079-9292/12/19/4091 |
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