Machine learning and deep learning approaches in IoT
The internet is a booming sector for exchanging information because of all the gadgets in today’s world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-02-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1204.pdf |
| _version_ | 1811168917250375680 |
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| author | Abqa Javed Muhammad Awais Muhammad Shoaib Khaldoon S. Khurshid Mahmoud Othman |
| author_facet | Abqa Javed Muhammad Awais Muhammad Shoaib Khaldoon S. Khurshid Mahmoud Othman |
| author_sort | Abqa Javed |
| collection | DOAJ |
| description | The internet is a booming sector for exchanging information because of all the gadgets in today’s world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privacy are the IoMT (Internet of Medical Things) and the IoV (Internet of Vehicles). IoMT and IoV devices monitor real-time healthcare and traffic trends to protect an individual’s life. With the proliferation of these devices comes a rise in security assaults and threats, necessitating the deployment of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers about the domain were searched, and the top 50 papers were selected. In addition, research objectives are specified concerning the problem, which leads to research questions. After evaluating the associated research, data is retrieved from digital archives. Furthermore, based on the findings of this SLR, a taxonomy of IoT subdomains has been given. This article also identifies the difficult areas and suggests ideas for further research in the IoT. |
| first_indexed | 2024-04-10T16:34:32Z |
| format | Article |
| id | doaj.art-6366ffe663d24e29ab1922a04f60e218 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-10T16:34:32Z |
| publishDate | 2023-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-6366ffe663d24e29ab1922a04f60e2182023-02-08T15:05:09ZengPeerJ Inc.PeerJ Computer Science2376-59922023-02-019e120410.7717/peerj-cs.1204Machine learning and deep learning approaches in IoTAbqa Javed0Muhammad Awais1Muhammad Shoaib2Khaldoon S. Khurshid3Mahmoud Othman4Department of Computer Science, University of Engineering and Technology, Lahore, Punjab, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, Punjab, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, Punjab, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, Punjab, PakistanComputer Science Department, Future University in Egypt, New Cairo, EgyptThe internet is a booming sector for exchanging information because of all the gadgets in today’s world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privacy are the IoMT (Internet of Medical Things) and the IoV (Internet of Vehicles). IoMT and IoV devices monitor real-time healthcare and traffic trends to protect an individual’s life. With the proliferation of these devices comes a rise in security assaults and threats, necessitating the deployment of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers about the domain were searched, and the top 50 papers were selected. In addition, research objectives are specified concerning the problem, which leads to research questions. After evaluating the associated research, data is retrieved from digital archives. Furthermore, based on the findings of this SLR, a taxonomy of IoT subdomains has been given. This article also identifies the difficult areas and suggests ideas for further research in the IoT.https://peerj.com/articles/cs-1204.pdfIoT (Internet of Things)IoMT (Internet of Medical Things)IoV (Internet of Vehicles)IPS (Intrusion Prevention System)Machine learningDeep learning |
| spellingShingle | Abqa Javed Muhammad Awais Muhammad Shoaib Khaldoon S. Khurshid Mahmoud Othman Machine learning and deep learning approaches in IoT PeerJ Computer Science IoT (Internet of Things) IoMT (Internet of Medical Things) IoV (Internet of Vehicles) IPS (Intrusion Prevention System) Machine learning Deep learning |
| title | Machine learning and deep learning approaches in IoT |
| title_full | Machine learning and deep learning approaches in IoT |
| title_fullStr | Machine learning and deep learning approaches in IoT |
| title_full_unstemmed | Machine learning and deep learning approaches in IoT |
| title_short | Machine learning and deep learning approaches in IoT |
| title_sort | machine learning and deep learning approaches in iot |
| topic | IoT (Internet of Things) IoMT (Internet of Medical Things) IoV (Internet of Vehicles) IPS (Intrusion Prevention System) Machine learning Deep learning |
| url | https://peerj.com/articles/cs-1204.pdf |
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