Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks

Authors: Ramy Maarouf, Danish Sattar, Ashraf Matrawy
Published: 2021-05-30

Source: https://arxiv.org/abs/2105.14564

PDF: https://arxiv.org/pdf/2105.14564

AIにより推定されたラベル

敵対的攻撃 ポイズニング 脆弱性評価手法

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

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.