Network Traffic Classification through Machine Learning methods in IoT Networks

IoT refers to a wide variety of embedded devices which are connected to the internet. These devices share data with each other by using wireless technology. The regular monitoring of IoT devices is mandatory for proper functioning of devices. Classification of network traffic is mandatory for QoS purpose. Implementation of QoS is mandatory for proper functioning of network. In this research TONIOT dataset is used for network traffic classification purpose. Dataset contains all the features related to network traffic. Network traces data is firstly preprocessed to extract significant features. after feature selection machine learning algorithms are applied on these features. ML steps like standardization, normalization and one hot encoding is applied on the dataset. At the end, we will evaluate the performance of our model with other state of the art algorithms. KNN algorithm achieves highest accuracy with 99. 9% classification rate.