Real-Time Application Identification Scheme and Evaluation Method Using Machine Learning

With the diversification of mobile applications, the implementation of priority control that meets the communication requirements of each diversifying application and network slicing technology in 5G is awaited, and the need for Identifying the application names of traffic is increasing. This study introduces an operational scheme and performance evaluation methodology for real-time application identification, utilizing the highaccuracy and updatable application identification system demonstrated in the authors' previous research. We focus on the trade-off between buffer time and identification accuracy, present the effectiveness of the method from the perspective of future users, while providing analysis for the purpose of slicing for a specific application or communication type.