IoT Traffic Multi-Classification Using Network and Statistical Features in a Smart Environment

Abstract

As the number of Internet of Things (IoT) devices and applications increases, the capacity of the IoT access networks is considerably stressed. This can create significant performance bottlenecks in various layers of an end-to-end communication path, including the scheduling of the spectrum, the resource requirements for processing the IoT data at the Edge and/or Cloud, and the attainable delay for critical emergency scenarios. Thus, it is required to classify or predict the time varying traffic characteristics of the IoT devices. However, this classification remains at large an open challenge. Most of the existing solutions are based on machine learning techniques, which nonetheless present high computational cost while non considering the fine-grained flow characteristics. To this end, in this paper we design a two-stage classification framework that utilizes both the network and statistical features to characterize the IoT devices in the context of a smart city. We firstly perform the data cleaning and preprocessing of the data and then analyze the dataset to extract the network and statistical features set for different types of IoT devices. The evaluation results show that the proposed classification can achieve 99% accuracy as compared to other techniques with Mathews Correlation Coefficient of 0.96.