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http://dx.doi.org/10.22937/IJCSNS.2022.22.5.16

IoT Security and Machine Learning  

Almalki, Sarah (Department of Computer Science, College of Computers and Information Technology, Taif University)
Alsuwat, Hatim (Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University)
Alsuwat, Emad (Department of Computer Science, College of Computers and Information Technology, Taif University)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.5, 2022 , pp. 103-114 More about this Journal
Abstract
The Internet of Things (IoT) is one of the fastest technologies that are used in various applications and fields. The concept of IoT will not only be limited to the fields of scientific and technical life but will also gradually spread to become an essential part of our daily life and routine. Before, IoT was a complex term unknown to many, but soon it will become something common. IoT is a natural and indispensable routine in which smart devices and sensors are connected wirelessly or wired over the Internet to exchange and process data. With all the benefits and advantages offered by the IoT, it does not face many security and privacy challenges because the current traditional security protocols are not suitable for IoT technologies. In this paper, we presented a comprehensive survey of the latest studies from 2018 to 2021 related to the security of the IoT and the use of machine learning (ML) and deep learning and their applications in addressing security and privacy in the IoT. A description was initially presented, followed by a comprehensive overview of the IoT and its applications and the basic important safety requirements of confidentiality, integrity, and availability and its application in the IoT. Then we reviewed the attacks and challenges facing the IoT. We also focused on ML and its applications in addressing the security problem on the IoT.
Keywords
Internet of Things; IoT security and privacy; machine learning; deep learning; IoT attack;
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