# Darknet Traffic Classification and Adversarial Attacks

arXiv: https://arxiv.org/abs/2206.06371 · HTML: https://mlsanity.com/paper/2206.06371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 12 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [How Does Batch Normalization Help Optimization?](https://mlsanity.com/paper/1805.11604/) (2018)
- [Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition](https://mlsanity.com/paper/2006.11538/) (2020)
- [Free-Text Keystroke Dynamics for User Authentication](https://mlsanity.com/paper/2107.07009/) (2021)
- [Auxiliary-Classifier GAN for Malware Analysis](https://mlsanity.com/paper/2107.01620/) (2021)
- [Darknet Traffic Big-Data Analysis and Network Management to Real-Time Automating the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework](https://mlsanity.com/paper/2102.08411/) (2021)

## Abstract (arXiv)

The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities. This research aims to improve darknet traffic detection by assessing Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Auxiliary-Classifier Generative Adversarial Networks (AC-GAN) for classification of such traffic and the underlying application types. We find that our RF model outperforms the state-of-the-art machine learning techniques used in prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our RF classifier, we obfuscate select application type classes to simulate realistic adversarial attack scenarios. We demonstrate that our best-performing classifier can be defeated by such attacks, and we consider ways to deal with such adversarial attacks.