  The classification of IoT traffic is important to improve the efficiency and
security of IoT-based networks. As the state-of-the-art classification methods
are based on Deep Learning, most of the current results require a large amount
of data to be trained. Thereby, 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 IoT Traffic Classification Transformer (ITCT), a novel
approach that utilizes the state-of-the-art transformer-based model named
TabTransformer. ITCT, 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 has been made publicly
available.
