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Title	The Era Of Internet Of Things: Towards Better Security Using Machine Learning
ID_Doc	55462
Authors	Abdulla H.; Al-Raweshidy H.; Awad W.
Year	2023
Published	2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023
DOI	http://dx.doi.org/10.1109/ITIKD56332.2023.10099608
Abstract	The IoT has various applications in various industries, including logistics tracking, healthcare, automotive, and smart cities. To prepare for a future in which the IoT is everywhere and accessible from anywhere, it is more necessary than ever to address significant IoT security concerns. Traditional methods of securing IoT networks, which include applying security in the form of a 'patch' against known vulnerabilities, are ineffective due to the growing scale of IoT networks, the characteristics of IoT devices, and the complexity of IoT networks. Machine Learning-based security systems and solutions have the potential to address the issues in traditional approaches to improve the security of the IoT Networks. In this paper, we show the existing challenges in securing IoT devices. We also explore the gaps in the research related to applying machine learning to securing IoT Devices. Through this research, we aim to encourage researchers to discover techniques to make the Internet of Things ecosystem safer. © 2023 IEEE.
Author Keywords	Anomaly Detection; Intrusion Detection System; IoT; Machine Learning; Security

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