IoT Network Traffic Classification Using Machine Learning Algorithms: An Experimental Analysis

In this project, classification of IoT network traffic using random forest classifier is proposed. More-dataset is utilized to separate the IoT traffic classification. Data preprocessing is the concept of changing the raw data into a clean data set. The dataset is preprocessed in order to check missing values, noisy data, and other inconsistencies before executing it to the algorithm. Extraction takes place using Chi-square based feature extraction. To improve classification, the extraction regions are processed using the Chi- Square technique to extract several features and select the necessary features. This project uses the effective Chi-square technique to find feature extraction and feature selection. Finally, the selected features are fed into an extra tree and random forest classifier for accurate classification. This project is implemented using Python software.