Security in IOT using machine learning methods

The IOT, in general, refers to devices being connected to a network. As a result, in order to protect network devices from security breaches, data must be secured. Because of the potential severity of the consequences of an IOT (IoT) failure, further investigation into IoT security vulnerabilities is warranted. The primary goal of IoT security is to protect user-privacy and confidentiality, as well as the architecture, communications, and technology that makes up an IoT ecosystem, as well as to ensure that the ecosphere services become inevitable. Since simulators, designers, and computation and analytic tools have become more widely available, research in IoT security has lately gained a lot of traction. An overview of the present level of IoT-security research, the essential tools, and IoT model and simulators are the core contribution of this study. Machine learning technologies will be used to identify attack threats in IoT security. Decision tree, Support Vector Machine (SVM), Bayes Algorithm, K-Nearest Neighbor (KNN), and Random Forest (RF) are the five most commonly used methods in IOT.