Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment
Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for...
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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/S1110016824011864 |
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| author | Mimouna Abdullah Alkhonaini Manal Abdullah Alohali Mohammed Aljebreen Majdy M. Eltahir Meshari H. Alanazi Ayman Yafoz Raed Alsini Alaa O. Khadidos |
| author_facet | Mimouna Abdullah Alkhonaini Manal Abdullah Alohali Mohammed Aljebreen Majdy M. Eltahir Meshari H. Alanazi Ayman Yafoz Raed Alsini Alaa O. Khadidos |
| author_sort | Mimouna Abdullah Alkhonaini |
| collection | DOAJ |
| description | Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets. |
| format | Article |
| id | doaj-art-fa8d1ffd662f42f69c47ebbae95a1faf |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-fa8d1ffd662f42f69c47ebbae95a1faf2025-01-29T05:00:04ZengElsevierAlexandria Engineering Journal1110-01682025-01-011124962Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environmentMimouna Abdullah Alkhonaini0Manal Abdullah Alohali1Mohammed Aljebreen2Majdy M. Eltahir3Meshari H. Alanazi4Ayman Yafoz5Raed Alsini6Alaa O. Khadidos7Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, 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 Systems, Applied College at Mahayil, King Khalid University, Saudi ArabiaDepartment of Computer Science, College of Sciences, Northern Border University, Arar, Saudi Arabia; Corresponding author.Department 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 ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaIntrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.http://www.sciencedirect.com/science/article/pii/S1110016824011864Intrusion detection systemSecurityCyberattacksBlockchainInternet of Things |
| spellingShingle | Mimouna Abdullah Alkhonaini Manal Abdullah Alohali Mohammed Aljebreen Majdy M. Eltahir Meshari H. Alanazi Ayman Yafoz Raed Alsini Alaa O. Khadidos Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment Alexandria Engineering Journal Intrusion detection system Security Cyberattacks Blockchain Internet of Things |
| title | Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment |
| title_full | Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment |
| title_fullStr | Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment |
| title_full_unstemmed | Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment |
| title_short | Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment |
| title_sort | sandpiper optimization with hybrid deep learning model for blockchain assisted intrusion detection in iot environment |
| topic | Intrusion detection system Security Cyberattacks Blockchain Internet of Things |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011864 |
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