Physical Access System Security of IoT Devices using Machine Learning Techniques
7 Pages Posted: 14 Jun 2019
Date Written: March 20, 2019
Abstract
With the advancement of critical technologies in the Internet of things (IoT), the various IoT applications (e.g., smart home, digital healthcare, smart grid, and smart city) become widely used in the world. Internet of Things (IoT) continues its run as one of the most popular technology and thus forms the most innovative and research field. IoT technology has been developed and advanced to an extent that it includes almost everything from just transferring information across each other to developing a completely smart automated system on its own and hence with this advancement it has become equally important to secure these devices and intelligent systems and address attacks such as jamming, spoofing, denial of service attacks, malware detection and unauthorized user detection (X. Li, R. Lu, X. Liang, and X. Shen, 2011). The challenges faced in IoT devices physical access architecture provide solutions to IoT devices and their developed infrastructure from cyber security attacks with the help of artificial intelligence, machine learning techniques including supervised learning, unsupervised learning, Q - learning techniques etc. and analyze their outcomes are discussed in the paper ( Z. Sheng, S. Yang, Y. Yu, and A. Vasilakos, 2013).
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