# Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks

arXiv: https://arxiv.org/abs/2105.14564 · HTML: https://mlsanity.com/paper/2105.14564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [A Review of Feature Selection Methods Based on Mutual Information](https://mlsanity.com/paper/1509.07577/) (2015)
- [Gradientless Descent: High-Dimensional Zeroth-Order Optimization](https://mlsanity.com/paper/1911.06317/) (2019)

## Built on by (most cited first, 4 of 4)

- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective](https://mlsanity.com/paper/2404.10796/) (2024)
- [Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matters](https://mlsanity.com/paper/2512.02276/) (2025)
- [A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic](https://mlsanity.com/paper/2607.17105/) (2026)

## 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.