An experimental study of different machine and deep learning techniques for classification of encrypted network traffic
ThankGod Obasi, M. Omair Shafiq · 2020
There is a continuous evolution in the technology industry with different types of devices being produced and connected to the internet. Multiple types of applications run on the different devices, thereby generating a complex and huge amount of traffic (i.e., Big Data) on the internet. This has made it difficult and challenging for different Internet Service Providers (ISPs) to maintain their service quality and keep their networks and services secure. It is important for service providers to have the ability to classify large and complex network traffic to help promote a better Quality of Service (QoS) and manage networks. In this paper, we utilize, apply and evaluate different machine and deep learning techniques for classification of encrypted network traffic to help in managing networks, and thereby, help in improving quality and security of network. A comparison between the different algorithms used is presented. The experiment results show that ANN+XGB, CNN+XGB, and CapsNet+XGB performed better than the LSTM+XGB and Ensemble model in the classification of encrypted network traffic with accuracies of 96%, 96%, 96%, 93%, and 95% respectively using a total of 23 statistical features. More statistical features were considered compared to other existing related works to improve the process of the classification and different hidden patterns associated with the statistical features. The results show the effectiveness of the machine and deep learning algorithms for the classififcation of encrypted network traffic into different categories.