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Machine learning and deep learning algorithms can be used to classifyencrypted Internet traffic. Classification of encrypted traffic can become morechallenging in the presence of adversarial attacks that target the learningalgorithms. In this paper, we focus on investigating the effectiveness ofdifferent evasion attacks and see how resilient machine and deep learningalgorithms are. Namely, we test C4.5 Decision Tree, K-Nearest Neighbor (KNN),Artificial Neural Network (ANN), Convolutional Neural Networks (CNN) andRecurrent Neural Networks (RNN). In most of our experimental results, deeplearning shows better resilience against the adversarial samples in comparisonto machine learning. Whereas, the impact of the attack varies depending on thetype of attack.

Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks