Abstract
The Internet of Things is a rapidly emerging technology (IoT). Thanks to numerous sensors, billions of smart objects (referred to as "Things") may acquire data about themselves and their environment. So they may regulate and monitor industrial services or increase commercial services and activities. However, the Internet of Things is now more vulnerable than ever. Machine Learning (ML) has advanced significantly, bringing up new research avenues to solve current and future IoT challenges. On the other hand, machine learning is an effective method for identifying peril in intelligent devices and networks. Following a thorough literature review on Machine Learning methods and the necessity of IoT security, this study will assess numerous ML algorithms for threat detection and the various security methods, which are associated with Machine Learning techniques.
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