SeAuNet: Semi-Autonomous Encrypted Traffic Classification and Self-labeling

With the more attention to user privacy and communication security, encrypted traffic has expanded substantially, which has brought huge challenges to traditional traffic classification methods. Deep learning knowledge has great advantages in processing encrypted traffic classification. However, it is difficult for researchers to realize unknown encrypted traffic classification in time, due to the complex parameters optimization process. In order to solve the problems mentioned above, we propose a semi-autonomous encrypted traffic classification and self-labeling scheme to (i) automatically and fast achieve architecture search for known encrypted traffic classification based on simulated annealing and particle swarm optimization, (ii) accomplish unknown encrypted traffic self-labeling based on siamese network, and build a corresponding training dataset, and (iii) update encrypted traffic classifier with transfer learning. Specifically, to validate the feasibility and robustness of the proposed scheme, four specific scenarios are tested based on an open dataset. The results demonstrate that our proposed scheme accomplishes neural architecture search with an average detection rate of up to 99%, provides correct labels for unknown encrypted traffic, and generates the latest dataset. Then, the classifier is updated successfully with the self-labeling encrypted traffic dataset.