# Generative Adversarial Classification Network with Application to Network Traffic Classification

arXiv: https://arxiv.org/abs/2303.10681 · HTML: https://mlsanity.com/paper/2303.10681/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [not-MIWAE: Deep Generative Modelling with Missing not at Random Data](https://mlsanity.com/paper/2006.12871/) (2020)
- [HexaGAN: Generative Adversarial Nets for Real World Classification](https://mlsanity.com/paper/1902.09913/) (2019)
- [Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case](https://mlsanity.com/paper/1801.03851/) (2018)

## Built on by (most cited first, 2 of 2)

- [MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification](https://mlsanity.com/paper/2411.13004/) (2024)
- [Integrated Imputation-Classification for Supervised Learning with Missing Data](https://mlsanity.com/paper/2610.04273/) (2026)

## Abstract (arXiv)

Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values. In this work, we are motivated by network traffic classification, where traditional data imputation methods do not perform well. We recognize that no existing method directly accounts for classification accuracy during data imputation. Therefore, we propose a joint data imputation and data classification method, termed generative adversarial classification network (GACN), whose architecture contains a generator network, a discriminator network, and a classification network, which are iteratively optimized toward the ultimate objective of classification accuracy. For the scenario where some data samples are unlabeled, we further propose an extension termed semi-supervised GACN (SSGACN), which is able to use the partially labeled data to improve classification accuracy. We conduct experiments with real-world network traffic data traces, which demonstrate that GACN and SS-GACN can more accurately impute data features that are more important for classification, and they outperform existing methods in terms of classification accuracy.