IoT refers to a wide variety of embedded devices that are connected to the internet. These devices share data by using wireless technology. The regular monitoring of IoT devices is mandatory for the proper functioning of devices. Classification of network traffic is mandatory for QoS purposes. Implementation of QoS is mandatory for the proper functioning of the network. For network traffic classification machine learning algorithms are used. The accuracy of machine learning algorithms depends on the data generated from IoT devices. In this research, the TON_IOT dataset is used for network traffic classification purposes. The 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 to these features. we then evaluate the performance of our model with other state-of-the-art algorithms. KNN algorithm achieves the highest accuracy with a 99.9% classification rate.