Network traffic classification and characterisation is playing an increasingly vital role in understanding and solving securityrelated issues in internet-based applications. The priority of research studies in this area has focused on characterisation of network traffic based on various layers of communication protocols as outlined in the TCP/IP stack and even further expanded to concentrate on specific application-layer protocols. Virtual Private Networks (VPNs) have become one of the most popular remote access communication methods among users over the public internet and other Internet Protocol (IP)-based networks. VPNs are governed by IP Security, which is a suite of protocols used for tunnelling the already encrypted IP traffic, to guarantee secure remote access to servers. In this paper, we propose and develop a framework to classify VPN or non-VPN network traffic using timerelated features. Our focus is on classification of network traffic which is encrypted, tunnelled through a VPN, and the one which is normally encrypted (non-VPN transmission), using machine-learning techniques on data sets of time-related features. Six classification models: logistic regression, support vector machine, Naïve Bayes, k-nearest neighbour and ensemble methodsthe Random Forest (RF) classifier and Gradient Boosting Tree (GBT) classifiersare compared, and recommendations of optimised RF and GBT models over other models are provided in terms of high accuracy and low overfitting. Features which contributed to achieve 90% accuracy in each category were also identified.