A Real Application Enabled Traffic Generator for Networking AI Model Development

Network measurement and management are more challenging in the next generation network systems due to the increasing demand for communications and complex network infrastructure. Recently, artificial intelligence (AI) algorithms have attracted much attention in networking systems, such as AI-based network traffic classification, traffic prediction, intrusion detection systems, etc. The development and maintenance of networking AI models usually require a large amount of traffic data samples from real applications. However, the publicly available datasets for network development are limited and rarely updated. In this paper, we develop a real application enabled traffic generator for AI model development in networking. In particular, a data loader is provided to establish two databases. One is a payload database that consists of packets from real applications. The other one is a traffic database that consists of network traffic flow statistics. The traffic generator allows a user to simulate data traffic flows that mimic one or more real applications. Moreover, two networking AI models are implemented to validate the simulated traffic flows. Evaluation results demonstrate that the developed traffic generator can help with networking AI model development.