Encrypted Application Classification with Convolutional Neural Network

Encrypted application classification (EAC) has become an emerging and challenging task for network monitoring and management, and statistical-based approaches are less impacted by encrypted streams. However, much effort is required from domain experts to handcraft statistical features. To solve this problem, this paper proposes an end-to-end encrypted application classification framework (E2E-EACF) based on one dimensional convolutional neural network (1D-CNN). Only encrypted payload (EncP) and inter-arrival time (IAT) are required by the framework to classify encrypted flows. Experimental results demonstrate that E2E-EACF can achieve more than 91.00% accuracy and 0.92 F1 score (the harmonic average of precision and recall) on a public dataset (WRCCDC), better than classical machine learning algorithms (e.g., decision tree and support vector machine).