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