mlsanityData to 7 Oct 2026

Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks

arXiv 2105.14564 ↗First posted 30 May 2021cs.CR cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 30 May 2021.

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12 of 42 references.

References · 12

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  4. 2016Adversarial examples in the physical worldcited by 1,762
  5. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  6. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  7. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  8. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  9. 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 211
  10. 2017Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learningcited by 69
  11. 2015A Review of Feature Selection Methods Based on Mutual Informationcited by 67
  12. 2019Gradientless Descent: High-Dimensional Zeroth-Order Optimizationcited by 42

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  2. 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
  3. 2025Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matterscited by 0
  4. 2026A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Trafficcited by 0