Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning
An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assis...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10196386/ |
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| author | Hayam Alamro Radwa Marzouk Nuha Alruwais Noha Negm Sumayh S. Aljameel Majdi Khalid Manar Ahmed Hamza Mohamed Ibrahim Alsaid |
| author_facet | Hayam Alamro Radwa Marzouk Nuha Alruwais Noha Negm Sumayh S. Aljameel Majdi Khalid Manar Ahmed Hamza Mohamed Ibrahim Alsaid |
| author_sort | Hayam Alamro |
| collection | DOAJ |
| description | An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models. |
| first_indexed | 2024-03-12T14:48:17Z |
| format | Article |
| id | doaj.art-dcfe46009a094e0dad4f747887b6bcaa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:48:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dcfe46009a094e0dad4f747887b6bcaa2023-08-15T23:00:37ZengIEEEIEEE Access2169-35362023-01-0111821998220710.1109/ACCESS.2023.329958910196386Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep LearningHayam Alamro0https://orcid.org/0000-0003-3157-8086Radwa Marzouk1https://orcid.org/0000-0001-6527-9856Nuha Alruwais2https://orcid.org/0009-0009-0119-869XNoha Negm3https://orcid.org/0009-0005-5911-1033Sumayh S. Aljameel4https://orcid.org/0000-0001-8246-4658Majdi Khalid5https://orcid.org/0000-0002-5397-0428Manar Ahmed Hamza6Mohamed Ibrahim Alsaid7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi ArabiaSaudi Aramco Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Systems, Umm Al-Qura University, Mecca, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaAn IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models.https://ieeexplore.ieee.org/document/10196386/Deep learningant lion optimizerInternet of Thingshealthcareblockchainsecurity |
| spellingShingle | Hayam Alamro Radwa Marzouk Nuha Alruwais Noha Negm Sumayh S. Aljameel Majdi Khalid Manar Ahmed Hamza Mohamed Ibrahim Alsaid Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning IEEE Access Deep learning ant lion optimizer Internet of Things healthcare blockchain security |
| title | Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning |
| title_full | Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning |
| title_fullStr | Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning |
| title_full_unstemmed | Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning |
| title_short | Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning |
| title_sort | modeling of blockchain assisted intrusion detection on iot healthcare system using ant lion optimizer with hybrid deep learning |
| topic | Deep learning ant lion optimizer Internet of Things healthcare blockchain security |
| url | https://ieeexplore.ieee.org/document/10196386/ |
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