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

arXiv: https://arxiv.org/abs/2109.14696 · HTML: https://mlsanity.com/paper/2109.14696/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 29 Sep 2021.

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

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [Deep Learning for Encrypted Traffic Classification: An Overview](https://mlsanity.com/paper/1810.07906/) (2018)
- [Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer](https://mlsanity.com/paper/1901.00525/) (2019)

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

- [Time-Distributed Feature Learning for Internet of Things Network Traffic Classification](https://mlsanity.com/paper/2409.05096/) (2024)

## Abstract (arXiv)

The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%.