A Comparative Study of Machine Learning-based Approach for Network Traffic Classification

Abstract

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques.

Network Slicing Based on Real-Time Traffic Classification in Software Defined Network (SDN) using Machine Learning

“…An open access VPN-Non_VPN (ISCXVPN2016) dataset was used by trang et al [10] to build a network traffic classification model. A two-scenarios testing process was created.…”

Performance Analysis of Random Forest on Quartile Classification Journal

“…Classification, a fundamental task within data mining, entails predicting the category to which a given dataset belongs [8]. While data mining-driven classification [9], has permeated various domains, encompassing manufacturing [10], agriculture [11], economics [12], education [13], and healthcare [14], the adaptation of these techniques to journal quartile classification remains an underexplored frontier. The landscape of classification models presents a rich tapestry, including K-Nearest Neighbors (KNN) [15], Support Vector Machines (SVM) [16], Naïve Bayes [17], Multi-Layer Perceptron (MLP) [18], and Random Forest [19].…”