Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment
Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owin...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/6/10/297 |
| _version_ | 1797473736003158016 |
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| author | Khalid A. Alissa Saud S. Alotaibi Fatma S. Alrayes Mohammed Aljebreen Sana Alazwari Hussain Alshahrani Mohamed Ahmed Elfaki Mahmoud Othman Abdelwahed Motwakel |
| author_facet | Khalid A. Alissa Saud S. Alotaibi Fatma S. Alrayes Mohammed Aljebreen Sana Alazwari Hussain Alshahrani Mohamed Ahmed Elfaki Mahmoud Othman Abdelwahed Motwakel |
| author_sort | Khalid A. Alissa |
| collection | DOAJ |
| description | Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques. |
| first_indexed | 2024-03-09T20:20:41Z |
| format | Article |
| id | doaj.art-b18b35405a5f49f294fc3c0ee5cd21f0 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T20:20:41Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-b18b35405a5f49f294fc3c0ee5cd21f02023-11-23T23:50:02ZengMDPI AGDrones2504-446X2022-10-0161029710.3390/drones6100297Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones EnvironmentKhalid A. Alissa0Saud S. Alotaibi1Fatma S. Alrayes2Mohammed Aljebreen3Sana Alazwari4Hussain Alshahrani5Mohamed Ahmed Elfaki6Mahmoud Othman7Abdelwahed Motwakel8Saudi Aramco Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 17472, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 17472, 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 ArabiaDrone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.https://www.mdpi.com/2504-446X/6/10/297internet of dronessecurityintrusion detectionmachine learningfeature selection |
| spellingShingle | Khalid A. Alissa Saud S. Alotaibi Fatma S. Alrayes Mohammed Aljebreen Sana Alazwari Hussain Alshahrani Mohamed Ahmed Elfaki Mahmoud Othman Abdelwahed Motwakel Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment Drones internet of drones security intrusion detection machine learning feature selection |
| title | Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment |
| title_full | Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment |
| title_fullStr | Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment |
| title_full_unstemmed | Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment |
| title_short | Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment |
| title_sort | crystal structure optimization with deep autoencoder based intrusion detection for secure internet of drones environment |
| topic | internet of drones security intrusion detection machine learning feature selection |
| url | https://www.mdpi.com/2504-446X/6/10/297 |
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