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

TL;DR

This paper introduces a novel approach for IoT network traffic classification by representing traffic data as images and videos, employing time-distributed neural networks to improve classification accuracy.

Contribution

It proposes a new data representation and a combined CNN-LSTM-TD MLP model for enhanced IoT network traffic classification performance.

Findings

Achieved a 10% improvement in classification accuracy.

Effectively captures intra-temporal and inter-flow features.

Utilizes large, multi-class dataset for validation.

Abstract

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%.