Towards a Transformer-Based Pre-trained Model for IoT Traffic Classification

The classification of IoT traffic is important to improve efficiency and security of IoT-based networks, and state-of-the-art classification methods are based on Deep Learning. However, most of the current results require a big amount of data to be trained. This way, in real life situations, where there is a scarce amount of IoT traffic data, the models would not perform so well. Consequently, these models underperform outside their initial training conditions and fail to capture the complex characteristics of network traffic, rendering them inefficient and unreliable in real-world applications. In this paper, we propose a novel IoT Traffic Classification Transformer (ITCT) approach, utilizing the state-of-the-art transformer-based model named TabTransformer. The model, which is pre-trained on a large labeled MQTT-based IoT traffic dataset and may be fine-tuned with a small set of labeled data, showed promising results in various traffic classification tasks. Our experiments demonstrated that the ITCT model significantly outperforms existing models, achieving an overall accuracy of 82%. To support reproducibility and collaborative development, all associated code is made publicly available.