Real-Time Application Identification Method for Mobile Networks 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 efficient learning method by packet selection focusing on TCP control flags and a method to guarantee real-time performance by time limitations of packet collection to implement a real-time application identification system. We have implemented an automatic collection system for training data, present the effectiveness of our methodology, and indicate a scheme for updating and operating future application identification systems.