Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics
The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfact...
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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/S1110016824011529 |
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| author | Rana Alabdan Bayan Alabduallah Nuha Alruwais Munya A. Arasi Somia A. Asklany Omar Alghushairy Fouad Shoie Alallah Abdulrhman Alshareef |
| author_facet | Rana Alabdan Bayan Alabduallah Nuha Alruwais Munya A. Arasi Somia A. Asklany Omar Alghushairy Fouad Shoie Alallah Abdulrhman Alshareef |
| author_sort | Rana Alabdan |
| collection | DOAJ |
| description | The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models. |
| format | Article |
| id | doaj-art-6e2c6355ddfb41f1bb6c5aaa31f56e9c |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-6e2c6355ddfb41f1bb6c5aaa31f56e9c2025-01-09T06:13:21ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110153167Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronicsRana Alabdan0Bayan Alabduallah1Nuha Alruwais2Munya A. Arasi3Somia A. Asklany4Omar Alghushairy5Fouad Shoie Alallah6Abdulrhman Alshareef7Department of Information Systems, College of Computer and Information Science, Majmaah University, Al-Majmaah 11952, 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 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, College of Science and Arts in RijalAlmaa, King Khalid University, Saudi ArabiaDepartment of Computer Science and Information Technology, Faculty of Sciences and Arts, Turaif, Northern Border University, Arar 91431, Saudi Arabia; Corresponding author.Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21589, 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 (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.http://www.sciencedirect.com/science/article/pii/S1110016824011529Consumer electronicsBlockchainInternet of thingsDeep learningIntrusion detection systemCrayfish optimization algorithm |
| spellingShingle | Rana Alabdan Bayan Alabduallah Nuha Alruwais Munya A. Arasi Somia A. Asklany Omar Alghushairy Fouad Shoie Alallah Abdulrhman Alshareef Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics Alexandria Engineering Journal Consumer electronics Blockchain Internet of things Deep learning Intrusion detection system Crayfish optimization algorithm |
| title | Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics |
| title_full | Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics |
| title_fullStr | Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics |
| title_full_unstemmed | Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics |
| title_short | Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics |
| title_sort | blockchain assisted improved interval type 2 fuzzy deep learning based attack detection on internet of things driven consumer electronics |
| topic | Consumer electronics Blockchain Internet of things Deep learning Intrusion detection system Crayfish optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011529 |
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