IOT Security and Machine Learning Algorithms: Survey and Analysis

The Internet of Things (IoT) establishes a web extension by combining physical components to arrange them self into shared objects. This creative observation can be utilized to transform a physical object mean something in the computerized world. The Internet of Things consists of devices that are constrained inside a resource such as rechargeable batteries, memory, as well as managing ability, and so forth. The Internet of Things aims to transform physical reality into artificially intelligent objects in the coming years. The Internet of Things (IoT) aims to combine all of that in the surroundings underneath a single infrastructure, giving us access to the objects around us maintaining us kept up-to-date on their situation. IoTs have a number of problems, including fading, energy use, data security, network security, etc. Machine Learning (ML) has also made substantial technological advances, opening up numerous study possibilities to address the present and future security challenges in IoT. ML is employed as a powerful tool to accomplish this objective, detecting threats and identifying abnormal behavior of intelligent networked devices. Following a thorough examination of the literature on IoT attacks and different machine learning methodologies, this survey article addresses the architecture of the IoT.