# Multitask Learning for Network Traffic Classification

arXiv: https://arxiv.org/abs/1906.05248 · HTML: https://mlsanity.com/paper/1906.05248/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification](https://mlsanity.com/paper/2107.04464/) (2021)
- [Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification](https://mlsanity.com/paper/2305.12432/) (2023)
- [Explanation Space: A New Perspective into Time Series Interpretability](https://mlsanity.com/paper/2409.01354/) (2024)
- [Website fingerprinting on early QUIC traffic](https://mlsanity.com/paper/2101.11871/) (2021)
- [Detecting Attacks on IoT Devices using Featureless 1D-CNN](https://mlsanity.com/paper/2109.03989/) (2021)
- [Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success](https://mlsanity.com/paper/2502.12930/) (2025)
- [Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting](https://mlsanity.com/paper/2601.21384/) (2026)
- [Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning](https://mlsanity.com/paper/2301.03532/) (2023)
- [Representation Learning of Tangled Key-Value Sequence Data for Early Classification](https://mlsanity.com/paper/2404.07454/) (2024)
- [FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification](https://mlsanity.com/paper/2404.08028/) (2024)
- [Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning](https://mlsanity.com/paper/2410.06140/) (2024)

## Abstract (arXiv)

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach.