Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities
Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene type...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823004040 |
| _version_ | 1797790864830889984 |
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| author | Mohammed Al-Jabbar Ebtesam Al-Mansor S. Abdel-Khalek Salem Alkhalaf |
| author_facet | Mohammed Al-Jabbar Ebtesam Al-Mansor S. Abdel-Khalek Salem Alkhalaf |
| author_sort | Mohammed Al-Jabbar |
| collection | DOAJ |
| description | Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene types for RS images has wide applications in several domains like urban planning, national defence security, environmental monitoring, and natural hazard detection. State-of-the-art deep learning (DL) successes are performed in a novel wave of RS scene classification applications, but it is the absence of explainability and trustworthiness. An intrusion detection system (IDS) plays a vital role to ensure security in the RS-based IoT environment. In this aspect, this study presents an ebola optimization algorithm with deep learning-based scene classification and intrusion detection (EOADL-SCID) technique on IoT-enabled remote sensing images. The aim of the EOADL-SCID system lies in the effectual scene classification of remote sensing images and intrusion detection. It involves a two-stage procedure. In the initial stage, the EOADL-SCID algorithm involves a modified DarkNet-53 feature extractor, EOA-based hyperparameter tuning, and graph convolution network (GCN) based classification. Next, in the second stage, the intrusion detection process takes place via two subprocesses namely variational autoencoder (VAE) based intrusion detection and skill optimization algorithm (SOA) based parameter tuning. The simulation outcomes of the EOADL-SCID approach are tested utilizing two benchmark databases and the experimental outcomes highlighted the improved performance of the EOADL-SCID algorithm on scene classification and intrusion classification processes. |
| first_indexed | 2024-03-13T02:10:36Z |
| format | Article |
| id | doaj.art-f4a161b01b3b4b6f8fc61caa8475fc67 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-13T02:10:36Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-f4a161b01b3b4b6f8fc61caa8475fc672023-07-01T04:34:25ZengElsevierAlexandria Engineering Journal1110-01682023-07-01752940Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart citiesMohammed Al-Jabbar0Ebtesam Al-Mansor1S. Abdel-Khalek2Salem Alkhalaf3Computer Sciences Department, Applied College, Najran University, Najran 66462, Saudi Arabia; Corresponding author.Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi ArabiaRemote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene types for RS images has wide applications in several domains like urban planning, national defence security, environmental monitoring, and natural hazard detection. State-of-the-art deep learning (DL) successes are performed in a novel wave of RS scene classification applications, but it is the absence of explainability and trustworthiness. An intrusion detection system (IDS) plays a vital role to ensure security in the RS-based IoT environment. In this aspect, this study presents an ebola optimization algorithm with deep learning-based scene classification and intrusion detection (EOADL-SCID) technique on IoT-enabled remote sensing images. The aim of the EOADL-SCID system lies in the effectual scene classification of remote sensing images and intrusion detection. It involves a two-stage procedure. In the initial stage, the EOADL-SCID algorithm involves a modified DarkNet-53 feature extractor, EOA-based hyperparameter tuning, and graph convolution network (GCN) based classification. Next, in the second stage, the intrusion detection process takes place via two subprocesses namely variational autoencoder (VAE) based intrusion detection and skill optimization algorithm (SOA) based parameter tuning. The simulation outcomes of the EOADL-SCID approach are tested utilizing two benchmark databases and the experimental outcomes highlighted the improved performance of the EOADL-SCID algorithm on scene classification and intrusion classification processes.http://www.sciencedirect.com/science/article/pii/S1110016823004040Remote sensingInternet of ThingsSecurityScene classificationIntrusion detectionEbola optimization algorithm |
| spellingShingle | Mohammed Al-Jabbar Ebtesam Al-Mansor S. Abdel-Khalek Salem Alkhalaf Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities Alexandria Engineering Journal Remote sensing Internet of Things Security Scene classification Intrusion detection Ebola optimization algorithm |
| title | Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities |
| title_full | Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities |
| title_fullStr | Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities |
| title_full_unstemmed | Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities |
| title_short | Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities |
| title_sort | ebola optimization with modified darknet 53 model for scene classification and security on internet of things in smart cities |
| topic | Remote sensing Internet of Things Security Scene classification Intrusion detection Ebola optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823004040 |
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