Machine Learning Methods of IoT Security and Future Application

Abstract

One of the technologies that are now expanding rapidly is called the Internet of Things (IoT). It is a technology that enables billions of smart devices or things, collectively referred to as “Things,” to collect a variety of data about themselves and the environment in which they are located using a variety of sensors. They can then share data with parties who have been permitted to do so for a variety of objectives, such as the management and monitoring of industrial services or the expansion of company services or operations. However, there are presently more security risks associated with the Internet of Things than ever. The field of machine learning (ML) has recently experienced significant advancement in technology, which has resulted in the opening of various new lines of inquiry that may be used to address existing and upcoming issues related to the Internet of Things. Nevertheless, machine learning is a robust technology that can recognize suspicious dangers and activities in smart devices and grids. The authors of this paper conducted an extensive literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. After that, they compared several different ML algorithms regarding the detection of attacks and anomalies. Additionally, many machines learning-based Internet of Things protection systems have been presented.

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