Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems
Security donates itself as one of the largest attacks on the support and development of the Internet of Things (IoT). Security challenges are evident in cyber-security threads that direct the main Internet service provider and weaken a significant part of the complete Internet by benefiting from def...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10720750/ |
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| author | Hayam Alamro Wahida Mansouri Kawther Saeedi Menwa Alshammeri Jawhara Aljabri Faiz Abdullah Alotaibi Noha Negm Mahir Mohammed Sharif |
| author_facet | Hayam Alamro Wahida Mansouri Kawther Saeedi Menwa Alshammeri Jawhara Aljabri Faiz Abdullah Alotaibi Noha Negm Mahir Mohammed Sharif |
| author_sort | Hayam Alamro |
| collection | DOAJ |
| description | Security donates itself as one of the largest attacks on the support and development of the Internet of Things (IoT). Security challenges are evident in cyber-security threads that direct the main Internet service provider and weaken a significant part of the complete Internet by benefiting from defective and vulnerable embedded gadgets. Numerous devices inhabit at-home systems with user-administrators unfamiliar with network security best practices, creating simple goals for the attackers. So, security solutions are required to direct the untrusted and insecure public networks by mechanizing defences over affordable and nearby direct network data sharing. The growth of automatic cyberattack classification and detection tools utilizing artificial intelligence (AI) and machine learning (ML) devices become vital to achieving safety in the IoT environment. It is desired that safety issues allied to IoT devices be effectively diminished. This article proposes an Advanced Ensemble Transfer Learning for Cyberthreat Detection in Low Power Systems (AETL-CDLPS) technique. The primary intention of the AETL-CDLPS technique is to automate the detection of cyber-attacks for IoT-assisted resource-constrained systems. The AETL-CDLPS technique utilizes a linear scaling normalization (LSN) model to normalize the input data. Next, the AETL-CDLPS technique employs an improved coati optimization algorithm (ICOA)-based feature selection technique to choose optimal features. For the cyber threat detection process, an ensemble transfer learning (TL) model comprises three classifiers, namely gated recurrent Unit (GRU), deep convolutional neural network (DCNN), and stacked sparse autoencoder (SSAE). Finally, the Bayesian optimization algorithm (BOA) is utilized to optimize the hyperparameter tuning of the three ensemble techniques. The AETL-CDLPS model’s performance validation is performed using the Bot-IoT dataset. The comparison study of the AETL-CDLPS method portrayed superiorAccuracy, Precision, Recall, and F-Score values of 99.19%, 96.10%, 95.97%, and 96.03% over existing models. |
| format | Article |
| id | doaj-art-7c867a0d3cda472981a0b86d0aa6257e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7c867a0d3cda472981a0b86d0aa6257e2024-12-04T00:00:30ZengIEEEIEEE Access2169-35362024-01-011217729817731110.1109/ACCESS.2024.348287610720750Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained SystemsHayam Alamro0https://orcid.org/0000-0003-3157-8086Wahida Mansouri1Kawther Saeedi2https://orcid.org/0000-0002-5295-4485Menwa Alshammeri3https://orcid.org/0000-0002-4645-3991Jawhara Aljabri4Faiz Abdullah Alotaibi5https://orcid.org/0009-0007-1908-4928Noha Negm6https://orcid.org/0009-0005-5911-1033Mahir Mohammed Sharif7https://orcid.org/0000-0003-2971-2987Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box. 84428, Riyadh, Saudi ArabiaDepartment of Computer Science and Information Technology, Faculty of Sciences and Arts, Northern Border University, Turaif, Arar, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, University College in Umluj, University of Tabuk, Tabuk, Saudi ArabiaDepartment of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaSecurity donates itself as one of the largest attacks on the support and development of the Internet of Things (IoT). Security challenges are evident in cyber-security threads that direct the main Internet service provider and weaken a significant part of the complete Internet by benefiting from defective and vulnerable embedded gadgets. Numerous devices inhabit at-home systems with user-administrators unfamiliar with network security best practices, creating simple goals for the attackers. So, security solutions are required to direct the untrusted and insecure public networks by mechanizing defences over affordable and nearby direct network data sharing. The growth of automatic cyberattack classification and detection tools utilizing artificial intelligence (AI) and machine learning (ML) devices become vital to achieving safety in the IoT environment. It is desired that safety issues allied to IoT devices be effectively diminished. This article proposes an Advanced Ensemble Transfer Learning for Cyberthreat Detection in Low Power Systems (AETL-CDLPS) technique. The primary intention of the AETL-CDLPS technique is to automate the detection of cyber-attacks for IoT-assisted resource-constrained systems. The AETL-CDLPS technique utilizes a linear scaling normalization (LSN) model to normalize the input data. Next, the AETL-CDLPS technique employs an improved coati optimization algorithm (ICOA)-based feature selection technique to choose optimal features. For the cyber threat detection process, an ensemble transfer learning (TL) model comprises three classifiers, namely gated recurrent Unit (GRU), deep convolutional neural network (DCNN), and stacked sparse autoencoder (SSAE). Finally, the Bayesian optimization algorithm (BOA) is utilized to optimize the hyperparameter tuning of the three ensemble techniques. The AETL-CDLPS model’s performance validation is performed using the Bot-IoT dataset. The comparison study of the AETL-CDLPS method portrayed superiorAccuracy, Precision, Recall, and F-Score values of 99.19%, 96.10%, 95.97%, and 96.03% over existing models.https://ieeexplore.ieee.org/document/10720750/Ensemble transfer learningcyberthreat detectionlow power systemsimproved coati optimization algorithmInternet of Things |
| spellingShingle | Hayam Alamro Wahida Mansouri Kawther Saeedi Menwa Alshammeri Jawhara Aljabri Faiz Abdullah Alotaibi Noha Negm Mahir Mohammed Sharif Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems IEEE Access Ensemble transfer learning cyberthreat detection low power systems improved coati optimization algorithm Internet of Things |
| title | Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems |
| title_full | Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems |
| title_fullStr | Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems |
| title_full_unstemmed | Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems |
| title_short | Modeling of Bayesian-Based Optimized Transfer Learning Model for Cyber-Attack Detection in Internet of Things Assisted Resource Constrained Systems |
| title_sort | modeling of bayesian based optimized transfer learning model for cyber attack detection in internet of things assisted resource constrained systems |
| topic | Ensemble transfer learning cyberthreat detection low power systems improved coati optimization algorithm Internet of Things |
| url | https://ieeexplore.ieee.org/document/10720750/ |
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