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

It is an effective tool to sequence. Our model can learn network dynamics from packet improve the efficiency and security of the Internet of Things traces, and the obtained results from the experiments are (IoT) ecosystem. The ability to classify IoT traffic accurately promising: ITCT demonstrates a remarkable ability to generalize empowers Internet Service Providers (ISPs) to furnish highquality across various prediction tasks and environments. Having services to network users, thereby ensuring optimal been evaluated with generic datasets, the model employs performance, security, and resource allocation [1]. Transformers to learn contextual embeddings of categorical Conventional traffic classification techniques, which distinguish features effectively. We have pre-trained this model using an various network services based on basic traffic characteristics MQTT-based IoT traffic dataset [14], enabling others to further like communication protocol and port number, are fine-tune it with their own data, regardless of the dataset's increasingly inadequate due to the complexity and changeability size. The results indicate the ITCT transformer's potential to of contemporary traffic [2], [3]. To overcome this achieve commendable evaluation metrics. For example, one of challenge, numerous studies have employed machine learning our proposed models attained an overall accuracy of 82%, a (ML) algorithms for traffic classification using statistical similar performance to other classifiers tested.