The Internet of Things (IoT) is a rapidly evolving technology. Interrelated devices and sensors transmitted data via the network to resolve issues and provide additional services. Hospital treatment, remote control of equipment, and M2M contact, among other services, are provided to users without the involvement of humans. It increases the availability of Internet-connected devices in our daily lives, bringing with several benefits as well as security risks. There are a various of ML approaches that can be utilized to protect IoT from various types of threats. Machine Learning (ML) methods are used to generate precise outputs from huge complex data, which can then be utilized to predict and identify risks in IoT network. This chapter, carried out a comparative review of previous researches and studies on attack identification using Machine language techniques. The objective of this work is to give a systematic study of ML techniques that can be utilized to create upgraded attack detection models for IoT frameworks

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