We develop a novel time series feature extraction technique to address the encrypted application classification problem. The proposed method consists of two main steps. First, we propose a feature engineering technique to extract significant attributes of the encrypted network traffic behavior through analyzing the time series of receiving packets. In the second step, a deep learning technique is developed to exploit the advantage of time series data samples in providing the strong representation of the encrypted network applications. To evaluate the efficiency of the proposed solution on the encrypted application traffic classification problem, we carry out intensive experiments on a raw network traffic dataset, namely VPN-nonVPN, with three conventional classifier metrics including Precision, Recall, and F1 score. The experimental results demonstrate that our proposed approach can significantly improve the performance in identifying encrypted application traffic in terms of accuracy and computation efficiency.

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