An Analytical Review on Classification of IoT Traffic and Channel Allocation Using Machine Learning Technique

Abstract

The growth of Internet of Things devices and technologies has given rise to a challenging new threat in the form of user data traffic flow. When there is insufficient channel allocation and network traffic measures in place, large volumes of sensitive data are at danger, and the transmission of data around the world can be slowed down by unwanted data. Cybercriminals have the potential to take use of this for evil ends. As a consequence of this, sophisticated mechanisms for assigning network channels and classifying network traffic are required. These mechanisms must be able to analyze and assign carriers to Internet of Things (IoT) network traffic in real time. We present a novel strategy based on machine learning for assigning channels in IoT networks and identifying data that is safe to use in order to get around this problem. The classification of Internet of Things (IoT) traffic networks and the allotment of channels for harmless data in huge network traffic could both benefit greatly from the application of this technology. The suggested approach makes use of deep learning technologies to perform operations at the network level, which results in a significant reduction in the amount of time spent on network classification and allocation of appropriate transmission medium for Benign traffic while also producing encouraging outcomes.