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Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach

Vu, L. and Thuy, H.V. and Nguyen, Q.U. and Ngoc, T.N. and Nguyen, D.N. and Hoang, D.T. and Dutkiewicz, E. (2018) Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach. In: 18th International Symposium on Communication and Information Technology, ISCIT 2018, 26 September 2018 through 29 September 2018.

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Abstract

We develop a novel time series feature extraction technique to address the encrypted traffic/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 by analyzing the time series of receiving packets. In the second step, we develop a deep learning-based technique to exploit the correlation of time series data samples of the encrypted network applications. To evaluate the efficiency of the proposed solution on the encrypted 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. © 2018 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/ISCIT.2018.8587975
Uncontrolled Keywords: Classification (of information); Cryptography; Efficiency; Long short-term memory; Time series analysis; Computation efficiency; Conventional classifier; Feature engineerings; Learning approach; LSTM; Network applications; Time series features; Traffic classification; Deep learning
Additional Information: Conference code: 143862. Language of original document: English. All Open Access, Green.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9467

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