Segregation of IoT Traffic with Machine Learning Techniques

Abstract

The Internet of Things (IoT) is emerging as a new infrastructure of 21st century. With the advent of cloud computing and evolution of IoT, the classification of traffic over IoT networks has attained significance importance due to rapid growth of users and devices. It is need of the hour to isolate the benign traffic from the malevolent traffic and to channelise the normal traffic to the intended destination to suffice the QoS requirements of the IoT users. A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic to the designated nodes for serving the needs of the users. In this manuscript, machine learning and deep neural networks-based approaches are proposed for segregating the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion over IoT channels. This paper also provides insights into the future research endeavors to channelise the normal traffic and to handle the malicious traffic