# Time-Distributed Feature Learning for Internet of Things Network Traffic Classification

arXiv: https://arxiv.org/abs/2409.05096 · HTML: https://mlsanity.com/paper/2409.05096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 8 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Deep Learning for Encrypted Traffic Classification: An Overview](https://mlsanity.com/paper/1810.07906/) (2018)
- [Virtual Reality Gaming on the Cloud: A Reality Check](https://mlsanity.com/paper/2109.10114/) (2021)
- [Segmented Learning for Class-of-Service Network Traffic Classification](https://mlsanity.com/paper/2208.01793/) (2022)
- [Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things](https://mlsanity.com/paper/2109.14696/) (2021)

## Built on by (most cited first, 1 of 1)

- [ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction](https://mlsanity.com/paper/2411.11894/) (2024)

## Abstract (arXiv)

Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource management for the Internet of Things (IoT) network. Holistic temporal features consist of inter-, intra-, and pseudo-temporal features within packets, between packets, and among flows, providing the maximum information on network services without depending on defined classes in a problem. Conventional spatio-temporal features in the current solutions extract only space and time information between packets and flows, ignoring the information within packets and flow for IoT traffic. Therefore, we propose a new, efficient, holistic feature extraction method for deep-learning-based NTC using time-distributed feature learning to maximize the accuracy of the NTC. We apply a time-distributed wrapper on deep-learning layers to help extract pseudo-temporal features and spatio-temporal features. Pseudo-temporal features are mathematically complex to explain since, in deep learning, a black box extracts them. However, the features are temporal because of the time-distributed wrapper; therefore, we call them pseudo-temporal features. Since our method is efficient in learning holistic-temporal features, we can extend our method to both conventional and CoS NTC. Our solution proves that pseudo-temporal and spatial-temporal features can significantly improve the robustness and performance of any NTC. We analyze the solution theoretically and experimentally on different real-world datasets. The experimental results show that the holistic-temporal time-distributed feature learning method, on average, is 13.5% more accurate than the state-of-the-art conventional and CoS classifiers.