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Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach | IEEE Conference Publication | IEEE Xplore

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


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 ...Show More

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.
Date of Conference: 26-29 September 2018
Date Added to IEEE Xplore: 27 December 2018
ISBN Information:
Conference Location: Bangkok, Thailand

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