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Title	Deep Learning Approach For Smart Home Security Using 5G Technology
ID_Doc	17823
Authors	Amanullah M.; Chaudhary S.; Yalini R.; Balaji M.; Vijaya Sudha M.; Dhanraj J.A.
Year	2024
Published	Artificial Intelligence, Blockchain, Computing and Security - Proceedings of the International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023, 1
DOI	http://dx.doi.org/10.1201/9781003393580-86
Abstract	With the advent of 5G technology, the entire world will soon be permanently connected, which will connect everything from the biggest megacities to the smallest internet of things. Such a connected hierarchy must integrate the internet of things, smart homes, and smart cities into a single, comprehensive infrastructure. In this research, a fourlayer design is proposed that employs technologies including 5G, the internet of things and deep learning that connect and interface various elements. The research that existing deep learning methods for IoT in smart homes. Deep learning has been used in numerous domains and provides outcomes that are superior to those of human specialists. Deep learning can be typically regarded as an essential move toward genuine AI. Numerous data flows are produced in the detecting areas as a result of such IoT’s development using the 5G standards and uploaded to the cloud for further analysis. Better IoT aid may even be achieved by integrating big data extraction and deep learning in efficient ways. High dependability standards, extremely low latencies, additional capacity, increased security, and high-speed user interaction required for the upcoming 5G network. Also consider future trends, unresolved problems, and insights related to employing deep learning techniques to improve IoT security. As a result, the adoption of deep learning algorithms in the 5G networks has the prospect of enhancing people’s way of life through automating and competence. © 2024 The Author(s).
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