Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics
Security in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014315 |
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| author | Hayam Alamro Mohammed Maray Jawhara Aljabri Saad Alahmari Monir Abdullah Jehad Saad Alqurni Faiz Abdullah Alotaibi Abdelmoneim Ali Mohamed |
| author_facet | Hayam Alamro Mohammed Maray Jawhara Aljabri Saad Alahmari Monir Abdullah Jehad Saad Alqurni Faiz Abdullah Alotaibi Abdelmoneim Ali Mohamed |
| author_sort | Hayam Alamro |
| collection | DOAJ |
| description | Security in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer, and save sensitive information. Employing regular software updates, secure authentication protocols, and strong encryption are indispensable approaches to guarantee the integrity and privacy of user details. Drones offer users a bird's-eye view that can be started and implemented anywhere and anytime. But, the malicious use of drones has developed among criminals and cyber-criminals. The possibility and frequency of these attacks are maximum, and their effect is highly unsafe and devastating. Thus, the desire for protective, preventive counter-measures and detective are needed. Intrusion detection utilizing deep learning (DL) drones control advanced neural network (NN) structures to improve security surveillance in dynamic outdoor environments. Prepared with sophisticated sensors and onboard processing abilities, these drones autonomously examine aerial imagery to identify and classify possible risks like suspicious activities, unauthorized personnel, or vehicles. DL approaches allow drones to learn complex patterns and anomalies in real-time, enabling quick response and proactive security procedures. This study introduces an enhanced Mathematical Modeling-based Blockchain with Mountain Gazelle Optimization and Attention to Deep Learning for Cybersecurity (MGOADL-CS) technique in the drone's platform. The MGOADL-CS method aims to improve cybersecurity using BC technology in the drone's environment by detecting attacks using optimal DL models. In the initial stage, the MGOADL-CS technique uses a linear scaling normalization (LSN) approach to normalize the input data. The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. Finally, the MGO-based hyperparameter tuning process is performed to adjust the hyperparameter values of the ALSTM-NN model. To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches. |
| format | Article |
| id | doaj-art-23d79e8b591349358f579fd09466fefc |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-23d79e8b591349358f579fd09466fefc2025-02-07T04:47:00ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113366377Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronicsHayam Alamro0Mohammed Maray1Jawhara Aljabri2Saad Alahmari3Monir Abdullah4Jehad Saad Alqurni5Faiz Abdullah Alotaibi6Abdelmoneim Ali Mohamed7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science, University College in Umluj, University of Tabuk, Saudi ArabiaDepartment of Computer Science, Applied College, Northern Border University, Arar, Saudi Arabia; Corresponding author.Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi ArabiaDepartment of Educational Technologies, College of Education, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Information Science, College of Humanities and Social Sciences, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi ArabiaDepartment of Information Systems, College of Computer & Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi ArabiaSecurity in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer, and save sensitive information. Employing regular software updates, secure authentication protocols, and strong encryption are indispensable approaches to guarantee the integrity and privacy of user details. Drones offer users a bird's-eye view that can be started and implemented anywhere and anytime. But, the malicious use of drones has developed among criminals and cyber-criminals. The possibility and frequency of these attacks are maximum, and their effect is highly unsafe and devastating. Thus, the desire for protective, preventive counter-measures and detective are needed. Intrusion detection utilizing deep learning (DL) drones control advanced neural network (NN) structures to improve security surveillance in dynamic outdoor environments. Prepared with sophisticated sensors and onboard processing abilities, these drones autonomously examine aerial imagery to identify and classify possible risks like suspicious activities, unauthorized personnel, or vehicles. DL approaches allow drones to learn complex patterns and anomalies in real-time, enabling quick response and proactive security procedures. This study introduces an enhanced Mathematical Modeling-based Blockchain with Mountain Gazelle Optimization and Attention to Deep Learning for Cybersecurity (MGOADL-CS) technique in the drone's platform. The MGOADL-CS method aims to improve cybersecurity using BC technology in the drone's environment by detecting attacks using optimal DL models. In the initial stage, the MGOADL-CS technique uses a linear scaling normalization (LSN) approach to normalize the input data. The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. Finally, the MGO-based hyperparameter tuning process is performed to adjust the hyperparameter values of the ALSTM-NN model. To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches.http://www.sciencedirect.com/science/article/pii/S1110016824014315Internet of thingsConsumer electronicsUAVMountain gazelle optimizationCybersecurityDeep learning |
| spellingShingle | Hayam Alamro Mohammed Maray Jawhara Aljabri Saad Alahmari Monir Abdullah Jehad Saad Alqurni Faiz Abdullah Alotaibi Abdelmoneim Ali Mohamed Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics Alexandria Engineering Journal Internet of things Consumer electronics UAV Mountain gazelle optimization Cybersecurity Deep learning |
| title | Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics |
| title_full | Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics |
| title_fullStr | Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics |
| title_full_unstemmed | Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics |
| title_short | Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics |
| title_sort | mathematical modelling based blockchain with attention deep learning model for cybersecurity in iot consumer electronics |
| topic | Internet of things Consumer electronics UAV Mountain gazelle optimization Cybersecurity Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014315 |
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