Amanullah, Mohamed Ahzam and Habeeb Mohamed, Riyaz Ahamed Ariyaluran and Nasaruddin, Fariza Hanum and Gani, Abdullah and Ahmed, Ejaz and Nainar, Abdul Salam Mohamed and Md Akim, Nazihah and Imran, Muhammad (2020) Deep learning and big data technologies for IoT security. Computer Communications, 151. pp. 495-517. ISSN 0140-3664, DOI https://doi.org/10.1016/j.comcom.2020.01.016.
Full text not available from this repository.Abstract
Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects.
| Item Type: | Article |
|---|---|
| Funders: | Aziz Bin Abdul Rahman, Mrs. Azlinda Tee Binti Md Azlan Tee |
| Uncontrolled Keywords: | Deep learning; Big data; IoT security |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology Faculty of Computer Science & Information Technology > Department of Information System |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 30 Nov 2023 06:33 |
| Last Modified: | 30 Nov 2023 06:33 |
| URI: | http://eprints.um.edu.my/id/eprint/36904 |
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