Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment
The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can sa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012080 |
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| author | Amal K. Alkhalifa Nuha Alruwais Wahida Mansouri Munya A. Arasi Mohammed Alliheedi Fouad Shoie Alallah Alaa O. Khadidos Abdulrhman Alshareef |
| author_facet | Amal K. Alkhalifa Nuha Alruwais Wahida Mansouri Munya A. Arasi Mohammed Alliheedi Fouad Shoie Alallah Alaa O. Khadidos Abdulrhman Alshareef |
| author_sort | Amal K. Alkhalifa |
| collection | DOAJ |
| description | The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches. |
| format | Article |
| id | doaj-art-4ea90b3eb86742bbb7d62cbb0d40abf1 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-4ea90b3eb86742bbb7d62cbb0d40abf12025-01-18T05:03:38ZengElsevierAlexandria Engineering Journal1110-01682025-01-01111148159Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environmentAmal K. Alkhalifa0Nuha Alruwais1Wahida Mansouri2Munya A. Arasi3Mohammed Alliheedi4Fouad Shoie Alallah5Alaa O. Khadidos6Abdulrhman Alshareef7Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P.O.Box 22459, Riyadh 11495, Saudi ArabiaDepartment of Computer Science and Information Technology, Faculty of Sciences and Arts, Turaif, Northern Border University, Arar 91431, Saudi Arabia; LETI laboratory, University of Sfax, Tunisia; Corresponding author at: Department of Computer Science and Information Technology, Faculty of Sciences and Arts, Turaif, Northern Border University, Arar 91431, Saudi ArabiaDepartment of Computer Science, Applied College at RijalAlmaa, King Khalid University, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information, Al-Baha University, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaThe Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.http://www.sciencedirect.com/science/article/pii/S1110016824012080Internet of ThingsCybersecurityDeep LearningHyperparameter TuningDung Beetle Optimization |
| spellingShingle | Amal K. Alkhalifa Nuha Alruwais Wahida Mansouri Munya A. Arasi Mohammed Alliheedi Fouad Shoie Alallah Alaa O. Khadidos Abdulrhman Alshareef Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment Alexandria Engineering Journal Internet of Things Cybersecurity Deep Learning Hyperparameter Tuning Dung Beetle Optimization |
| title | Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment |
| title_full | Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment |
| title_fullStr | Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment |
| title_full_unstemmed | Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment |
| title_short | Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment |
| title_sort | hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on iot environment |
| topic | Internet of Things Cybersecurity Deep Learning Hyperparameter Tuning Dung Beetle Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824012080 |
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